Search results for: image analysis.

Authors: K.Somasundaram, I.Kaspar Raj

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In this paper we have proposed three and two stage still gray scale image compressor based on BTC. In our schemes, we have employed a combination of four techniques to reduce the bit rate. They are quad tree segmentation, bit plane omission, bit plane coding using 32 visual patterns and interpolative bit plane coding. The experimental results show that the proposed schemes achieve an average bit rate of 0.46 bits per pixel (bpp) for standard gray scale images with an average PSNR value of 30.25, which is better than the results from the exiting similar methods based on BTC.

Keywords: Bit plane, Block Truncation Coding, Image compression, lossy compression, quad tree segmentation

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Authors: A. A. Shafie, Fadhlan Hafiz, M. H. Ali

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Motion detection is very important in image processing. One way of detecting motion is using optical flow. Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. The method used for finding the optical flow in this project is assuming that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. This technique is later used in developing software for motion detection which has the capability to carry out four types of motion detection. The motion detection software presented in this project also can highlight motion region, count motion level as well as counting object numbers. Many objects such as vehicles and human from video streams can be recognized by applying optical flow technique.

Keywords: Background modeling, Motion detection, Optical flow, Velocity smoothness constant, motion trajectories.

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Authors: Kuo-Cheng Liu

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In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.

Keywords: thresholding technique, tamper proofing, tamper recovery

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Authors: J. Mehena, M. C. Adhikary

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In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.

Keywords: Clustering, fuzzy C-means, image segmentation, MR images, multiple kernels.

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Authors: K. Idir, H. Merouani, Y. Tlili.

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An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence.

Keywords: Dual Pyramid, Image Segmentation, Multi-agent System, Region/Edge Cooperation.

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Authors: Sangbum Park, Namki Lee, Youngjoon Han, Hernsoo Hahn

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In this paper, we propose the robust water level detection method based on the accumulated histogram under small changed image which is acquired from water level surveillance camera. In general surveillance system, this is detecting and recognizing invasion from searching area which is in big change on the sequential images. However, in case of a water level detection system, these general surveillance techniques are not suitable due to small change on the water surface. Therefore the algorithm introduces the accumulated histogram which is emphasizing change of water surface in sequential images. Accumulated histogram is based on the current image frame. The histogram is cumulating differences between previous images and current image. But, these differences are also appeared in the land region. The band pass filter is able to remove noises in the accumulated histogram Finally, this algorithm clearly separates water and land regions. After these works, the algorithm converts from the water level value on the image space to the real water level on the real space using calibration table. The detected water level is sent to the host computer with current image. To evaluate the proposed algorithm, we use test images from various situations.

Keywords: accumulated histogram, water level detection, band pass filter.

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Authors: Dipeng Chen, Qi Li

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Image fusion aims to enhance the perception of a scene by combining important information captured by different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been thouroughly investigated for image fusion, since it takes advantages of approximate shift invariance and direction selectivity. But it can only handle limited direction information. To allow a more flexible directional expansion for images, we propose a novel fusion scheme, referred to as complex contourlet transform (CCT). It successfully incorporates directional filter banks (DFB) into DT-CWT. As a result it efficiently deal with images containing contours and textures, whereas it retains the property of shift invariance. Experimental results demonstrated that the method features high quality fusion performance and can facilitate many image processing applications.

Keywords: Complex contourlet transform, Complex wavelettransform, Fusion.

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Authors: Oscar J.G. Somsen, Fok Bolderheij

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Electro-optical devices are increasingly used for military sea-, land- and air applications to detect, recognize and track objects. Typically, these devices produce video information that is presented to an operator. However, with increasing availability of electro-optical devices the data volume is becoming very large, creating a rising need for automated analysis. In a military setting, this typically involves detecting and recognizing objects at a large distance, i.e. when they are difficult to distinguish from background and noise. One may consider combining multiple images from a video stream into a single enhanced image that provides more information for the operator. In this paper we investigate a simple algorithm to enhance simulated images from a military context and investigate how the enhancement is affected by various types of disturbance.

Keywords: Electro-Optics, Automated Image alignment

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Authors: Stephan Rupp, Matthias Elter, Michael Breitung, Walter Zink, Christian Küblbeck

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Camera calibration is an indispensable step for augmented reality or image guided applications where quantitative information should be derived from the images. Usually, a camera calibration is obtained by taking images of a special calibration object and extracting the image coordinates of projected calibration marks enabling the calculation of the projection from the 3d world coordinates to the 2d image coordinates. Thus such a procedure exhibits typical steps, including feature point localization in the acquired images, camera model fitting, correction of distortion introduced by the optics and finally an optimization of the model-s parameters. In this paper we propose to extend this list by further step concerning the identification of the optimal subset of images yielding the smallest overall calibration error. For this, we present a Monte Carlo based algorithm along with a deterministic extension that automatically determines the images yielding an optimal calibration. Finally, we present results proving that the calibration can be significantly improved by automated image selection.

Keywords: Camera Calibration, Discrete Optimization, Monte Carlo Method.

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Authors: R. Sullivan, T. Holden, G. Tremberger, Jr, E. Cheung, C. Branch, J. Burrero, G. Surpris, S. Quintana, A. Rameau, N. Gadura, H. Yao, R. Subramaniam, P. Schneider, S. A. Rotenberg, P. Marchese, A. Flamhlolz, D. Lieberman, T. Cheung

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Migration in breast cancer cell wound healing assay had been studied using image fractal dimension analysis. The migration of MDA-MB-231 cells (highly motile) in a wound healing assay was captured using time-lapse phase contrast video microscopy and compared to MDA-MB-468 cell migration (moderately motile). The Higuchi fractal method was used to compute the fractal dimension of the image intensity fluctuation along a single pixel width region parallel to the wound. The near-wound region fractal dimension was found to decrease three times faster in the MDA-MB- 231 cells initially as compared to the less cancerous MDA-MB-468 cells. The inner region fractal dimension was found to be fairly constant for both cell types in time and suggests a wound influence range of about 15 cell layer. The box-counting fractal dimension method was also used to study region of interest (ROI). The MDAMB- 468 ROI area fractal dimension was found to decrease continuously up to 7 hours. The MDA-MB-231 ROI area fractal dimension was found to increase and is consistent with the behavior of a HGF-treated MDA-MB-231 wound healing assay posted in the public domain. A fractal dimension based capacity index has been formulated to quantify the invasiveness of the MDA-MB-231 cells in the perpendicular-to-wound direction. Our results suggest that image intensity fluctuation fractal dimension analysis can be used as a tool to quantify cell migration in terms of cancer severity and treatment responses.

Keywords: Higuchi fractal dimension, box-counting fractal dimension, cancer cell migration, wound healing.

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Authors: Mahmoud Saeidi, Khadijeh Saeidi, Mahmoud Khaleghi

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In this paper, we propose a novel spatiotemporal fuzzy based algorithm for noise filtering of image sequences. Our proposed algorithm uses adaptive weights based on a triangular membership functions. In this algorithm median filter is used to suppress noise. Experimental results show when the images are corrupted by highdensity Salt and Pepper noise, our fuzzy based algorithm for noise filtering of image sequences, are much more effective in suppressing noise and preserving edges than the previously reported algorithms such as [1-7]. Indeed, assigned weights to noisy pixels are very adaptive so that they well make use of correlation of pixels. On the other hand, the motion estimation methods are erroneous and in highdensity noise they may degrade the filter performance. Therefore, our proposed fuzzy algorithm doesn-t need any estimation of motion trajectory. The proposed algorithm admissibly removes noise without having any knowledge of Salt and Pepper noise density.

Keywords: Image Sequences, Noise Reduction, fuzzy algorithm, triangular membership function

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Authors: Krishnamoorthy R, Rajavijayalakshmi K, Punidha R

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In this paper, a near lossless image coding scheme based on Orthogonal Polynomials Transform (OPT) has been presented. The polynomial operators and polynomials basis operators are obtained from set of orthogonal polynomials functions for the proposed transform coding. The image is partitioned into a number of distinct square blocks and the proposed transform coding is applied to each of these individually. After applying the proposed transform coding, the transformed coefficients are rearranged into a sub-band structure. The Embedded Zerotree (EZ) coding algorithm is then employed to quantize the coefficients. The proposed transform is implemented for various block sizes and the performance is compared with existing Discrete Cosine Transform (DCT) transform coding scheme.

Keywords: Near-lossless Coding, Orthogonal Polynomials Transform, Embedded Zerotree Coding

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Authors: Hariton N. Costin, Iulian Ciocoiu, Tudor Barbu, Cristian Rotariu

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In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. The techniques under investigation are: a) Local Non-negative Matrix Factorization (LNMF); b) Independent Components Analysis (ICA); c) NMF with sparse constraints (NMFsc); d) Locality Preserving Projections (Laplacianfaces). Fingerprint detection was approached by classical minutiae (small graphical patterns) matching through image segmentation by using a structural approach and a neural network as decision block. As to voice / speaker recognition, melodic cepstral and delta delta mel cepstral analysis were used as main methods, in order to construct a supervised speaker-dependent voice recognition system. The final decision (e.g. “accept-reject" for a verification task) is taken by using a majority voting technique applied to the three biometrics. The preliminary results, obtained for medium databases of fingerprints, faces and voice recordings, indicate the feasibility of our study and an overall recognition precision (about 92%) permitting the utilization of our system for a future complex biometric card.

Keywords: Biometry, image processing, pattern recognition, speech analysis.

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Authors: Satyanadh Gundimada, Vijayan K Asari

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A novel feature selection strategy to improve the recognition accuracy on the faces that are affected due to nonuniform illumination, partial occlusions and varying expressions is proposed in this paper. This technique is applicable especially in scenarios where the possibility of obtaining a reliable intra-class probability distribution is minimal due to fewer numbers of training samples. Phase congruency features in an image are defined as the points where the Fourier components of that image are maximally inphase. These features are invariant to brightness and contrast of the image under consideration. This property allows to achieve the goal of lighting invariant face recognition. Phase congruency maps of the training samples are generated and a novel modular feature selection strategy is implemented. Smaller sub regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are arranged in the order of increasing distance between the sub regions involved in merging. The assumption behind the proposed implementation of the region merging and arrangement strategy is that, local dependencies among the pixels are more important than global dependencies. The obtained feature sets are then arranged in the decreasing order of discriminating capability using a criterion function, which is the ratio of the between class variance to the within class variance of the sample set, in the PCA domain. The results indicate high improvement in the classification performance compared to baseline algorithms.

Keywords: Discriminant analysis, intra-class probability distribution, principal component analysis, phase congruency.

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Authors: Noor Elaiza Abdul Khalid, Noorhayati Mohamed Noor, Zamalia Mahmud, Saadiah Yahya, and Norharyati Md Ariff

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Glaucoma diagnosis involves extracting three features of the fundus image; optic cup, optic disc and vernacular. Present manual diagnosis is expensive, tedious and time consuming. A number of researches have been conducted to automate this process. However, the variability between the diagnostic capability of an automated system and ophthalmologist has yet to be established. This paper discusses the efficiency and variability between ophthalmologist opinion and digital technique; threshold. The efficiency and variability measures are based on image quality grading; poor, satisfactory or good. The images are separated into four channels; gray, red, green and blue. A scientific investigation was conducted on three ophthalmologists who graded the images based on the image quality. The images are threshold using multithresholding and graded as done by the ophthalmologist. A comparison of grade from the ophthalmologist and threshold is made. The results show there is a small variability between result of ophthalmologists and digital threshold.

Keywords: Digital Fundus Image, Glaucoma Detection, Multithresholding, Segmentation.

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Authors: Dipti Patra, Mridula J

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In this paper, we propose a new image segmentation approach for colour textured images. The proposed method for image segmentation consists of two stages. In the first stage, textural features using gray level co-occurrence matrix(GLCM) are computed for regions of interest (ROI) considered for each class. ROI acts as ground truth for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Statistical mean feature at certain inter pixel distance (IPD) of I2 component was considered to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and modeled as Markov Random Field (MRF) model to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of the existing schemes, JSEG and another scheme which uses GLCM and MRF in RGB colour space. The proposed method is found to be outperforming the existing ones in terms of segmentation accuracy with acceptable rate of convergence. The results are validated with synthetic and real textured images.

Keywords: Texture Image Segmentation, Gray Level Cooccurrence Matrix, Markov Random Field Model, Ohta colour space, ICM algorithm.

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: AlexNet, Deep learning, image recognition, 6D posture estimation.

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Authors: H. Dujmic, V. Papic, H. Turic

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The article presents a new method for detection of artificial objects and materials from images of the environmental (non-urban) terrain. Our approach uses the hue and saturation (or Cb and Cr) components of the image as the input to the segmentation module that uses the mean shift method. The clusters obtained as the output of this stage have been processed by the decision-making module in order to find the regions of the image with the significant possibility of representing human. Although this method will detect various non-natural objects, it is primarily intended and optimized for detection of humans; i.e. for search and rescue purposes in non-urban terrain where, in normal circumstances, non-natural objects shouldn-t be present. Real world images are used for the evaluation of the method.

Keywords: Landscape surveillance, mean shift algorithm, image segmentation, target detection.

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Authors: Sundararajan Sangeetha, Joseph Jesu Christopher, Swaminathan Ramakrishnan

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In this work, the primary compressive strength components of human femur trabecular bone are qualitatively assessed using image processing and wavelet analysis. The Primary Compressive (PC) component in planar radiographic femur trabecular images (N=50) is delineated by semi-automatic image processing procedure. Auto threshold binarization algorithm is employed to recognize the presence of mineralization in the digitized images. The qualitative parameters such as apparent mineralization and total area associated with the PC region are derived for normal and abnormal images.The two-dimensional discrete wavelet transforms are utilized to obtain appropriate features that quantify texture changes in medical images .The normal and abnormal samples of the human femur are comprehensively analyzed using Harr wavelet.The six statistical parameters such as mean, median, mode, standard deviation, mean absolute deviation and median absolute deviation are derived at level 4 decomposition for both approximation and horizontal wavelet coefficients. The correlation coefficient of various wavelet derived parameters with normal and abnormal for both approximated and horizontal coefficients are estimated. It is seen that in almost all cases the abnormal show higher degree of correlation than normals. Further the parameters derived from approximation coefficient show more correlation than those derived from the horizontal coefficients. The parameters mean and median computed at the output of level 4 Harr wavelet channel was found to be a useful predictor to delineate the normal and the abnormal groups.

Keywords: Image processing, planar radiographs, trabecular bone and wavelet analysis.

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Authors: Abdulnasir Hossen, Ulrich Heute

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In 1990 [1] the subband-DFT (SB-DFT) technique was proposed. This technique used the Hadamard filters in the decomposition step to split the input sequence into low- and highpass sequences. In the next step, either two DFTs are needed on both bands to compute the full-band DFT or one DFT on one of the two bands to compute an approximate DFT. A combination network with correction factors was to be applied after the DFTs. Another approach was proposed in 1997 [2] for using a special discrete wavelet transform (DWT) to compute the discrete Fourier transform (DFT). In the first step of the algorithm, the input sequence is decomposed in a similar manner to the SB-DFT into two sequences using wavelet decomposition with Haar filters. The second step is to perform DFTs on both bands to obtain the full-band DFT or to obtain a fast approximate DFT by implementing pruning at both input and output sides. In this paper, the wavelet-based DFT (W-DFT) with Haar filters is interpreted as SB-DFT with Hadamard filters. The only difference is in a constant factor in the combination network. This result is very important to complete the analysis of the W-DFT, since all the results concerning the accuracy and approximation errors in the SB-DFT are applicable. An application example in spectral analysis is given for both SB-DFT and W-DFT (with different filters). The adaptive capability of the SB-DFT is included in the W-DFT algorithm to select the band of most energy as the band to be computed. Finally, the W-DFT is extended to the two-dimensional case. An application in image transformation is given using two different types of wavelet filters.

Keywords: Image Transform, Spectral Analysis, Sub-Band DFT, Wavelet DFT.

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Authors: Amine Naït-Ali

Abstract:

The aim of this paper is to present a new method which can be used for progressive transmission of electrocardiogram (ECG). The idea consists in transforming any ECG signal to an image, containing one beat in each row. In the first step, the beats are synchronized in order to reduce the high frequencies due to inter-beat transitions. The obtained image is then transformed using a discrete version of Radon Transform (DRT). Hence, transmitting the ECG, leads to transmit the most significant energy of the transformed image in Radon domain. For decoding purpose, the receptor needs to use the inverse Radon Transform as well as the two synchronization frames. The presented protocol can be adapted for lossy to lossless compression systems. In lossy mode we show that the compression ratio can be multiplied by an average factor of 2 for an acceptable quality of reconstructed signal. These results have been obtained on real signals from MIT database.

Keywords: Discrete Radon Transform, ECG compression, synchronization.

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Authors: Stephen Karungaru, Minoru Fukumi, Norio Akamatsu

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In this paper, we propose a morphing method by which face color images can be freely transformed. The main focus of this work is the transformation of one face image to another. This method is fully automatic in that it can morph two face images by automatically detecting all the control points necessary to perform the morph. A face detection neural network, edge detection and medium filters are employed to detect the face position and features. Five control points, for both the source and target images, are then extracted based on the facial features. Triangulation method is then used to match and warp the source image to the target image using the control points. Finally color interpolation is done using a color Gaussian model that calculates the color for each particular frame depending on the number of frames used. A real coded Genetic algorithm is used in both the image warping and color blending steps to assist in step size decisions and speed up the morphing. This method results in ''very smooth'' morphs and is fast to process.

Keywords: color transition, genetic algorithms morphing, warping

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Authors: Hasup Lee, Yoshisuke Tateyama, Tetsuro Ogi

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We developed a vision interface immersive projection system, CAVE in virtual rea using hand gesture recognition with computer vis background image was subtracted from current webcam and we convert the color space of the imag Then we mask skin regions using skin color range t a noise reduction operation. We made blobs fro gestures were recognized using these blobs. Using recognition, we could implement an effective bothering devices for CAVE. e framework for an reality research field vision techniques. ent image frame age into HSV space. e threshold and apply from the image and ing our hand gesture e interface without

Keywords: CAVE, Computer Vision, Ges Virtual Reality esture Recognition,

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Authors: Ikilem Gocek, Senem Kursun, Yesim Iridag Beceren

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The customer satisfaction for textile sector carries great importance like the customer satisfaction for other sectors carry. Especially, if it is considered that gaining new customers create four times more costs than protecting existing customers from leaving, it can be seen that the customer satisfaction plays a great role for the firms. In this study the affecting independent variables of customer satisfaction are chosen as brand image, perceived service quality and perceived product quality. By these independent variables, it is investigated that if any differences exist in perception of customer satisfaction according to the Turkish textile consumers in the view of gender. In data analysis of this research the SPSS program is used.

Keywords: Customer satisfaction, textile industry, brand image, service quality, product quality, gender.

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Authors: Navdeep Goel, Salvador Gabarda

Abstract:

Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4 × 4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4 × 4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4 × 4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: Chirp signals, Image multiplexing, Image transformation, Linear canonical transform, Polynomial approximation.

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Authors: P. J. Borkowski, J. A. Borkowski

Abstract:

The paper presents a novel method for the 3D shaping of different materials using a high-pressure abrasive water jet and a flat target image. For steering movement process of the jet a principle similar to raster image way of record and readout was used. However, respective colors of pixel of such a bitmap are connected with adequate jet feed rate that causes erosion of material with adequate depth. Thanks to that innovation, one can observe spatial imaging of the object. Theoretical basis as well as spatial model of material shaping and experimental stand including steering program are presented in. There are also presented methodic and some experimental erosion results as well as practical example of object-s bas-relief made of metal.

Keywords: High-pressure, abrasive, water jet, material shaping.

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

Abstract:

The detection and segmentation of mitochondria from fluorescence microscopy is crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. Although there exists a number of open-source software tools and artificial intelligence (AI) methods designed for analyzing mitochondrial images, the availability of only a few combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compactibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source Python and OpenCV library, the algorithms are implemented in three stages: pre-processing; image binarization; and coarse-to-fine segmentation. The proposed model is validated using the fluorescence mitochondrial dataset. Ground truth labels generated using Labkit were also used to evaluate the performance of our detection and segmentation model using precision, recall and rand index. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks concludes the paper.

Keywords: 2D, Binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation.

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Authors: Gavin Perin, Linda Matthews

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Webcam systems now function as the new privileged vantage points from which to view the city. This transformation of CCTV technology from surveillance to promotional tool is significant because its'scopic regime' presents, back to the public, a new virtual 'site' that sits alongside its real-time counterpart. Significantly, thisraw 'image' data can, in fact,be co-optedand processed so as to disrupt their original purpose. This paper will demonstrate this disruptive capacity through an architectural project. It will reveal how the adaption the webcam image offers a technical springboard by which to initiate alternate urban form making decisions and subvert the disciplinary reliance on the 'flat' orthographic plan. In so doing, the paper will show how this 'digital material' exceeds the imagistic function of the image; shiftingit from being a vehicle of signification to a site of affect.

Keywords: Surveillance, virtual, scopic, additive

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Authors: Sangbum Park, Youngjoon Han

Abstract:

This paper proposes a balance control scheme for a biped robot to trace an arbitrary path using image information. While moving, it estimates the zero moment point(ZMP) of the biped robot in the next step using a Kalman filter and renders an appropriate balanced pose of the robot. The ZMP can be calculated from the robot's pose, which is measured from the reference object image acquired by a CCD camera on the robot's head. For simplifying the kinematical model, the coordinates systems of individual joints of each leg are aligned and the robot motion is approximated as an inverted pendulum so that a simple linear dynamics, 3D-LIPM(3D-Linear Inverted Pendulum Mode) can be applied. The efficiency of the proposed algorithm has been proven by the experiments performed on unknown trajectory.

Keywords: Biped robot, Zero moment point, Balance control, Kalman filter.

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