Search results for: Fractal Image Compression

Authors: Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin

Abstract:

Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect Recognizer which uses computer vision methodology with the combination of multi-layer neural networks to identify four classifications of textile defects. The recognizer, suitable for LDC countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. In order to generate input set for the neural network, primarily the recognizer captures digital fabric images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the output of the processed image, the area of the faulty portion, the number of objects of the image and the sharp factor of the image, are feed backed as an input layer to the neural network which uses back propagation algorithm to compute the weighted factors and generates the desired classifications of defects as an output.

Keywords: Computer vision, image acquisition device, machine vision, multi-layer neural networks.

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Authors: Bence Kővári, ZSolt Kertész

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This paper proposes a stroke extraction method for use in off-line signature verification. After giving a brief overview of the current ongoing researches an algorithm is introduced for detecting and following strokes in static images of signatures. Problems like the handling of junctions and variations in line width and line intensity are discussed in detail. Results are validated by both using an existing on-line signature database and by employing image registration methods.

Keywords: Stroke extraction, spline fitting, off-line signatureverification, image registration.

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies  the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: Retail stores, Faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition.

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Authors: Hossein Mirzaee, Farhad Besharati

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Solar sunspot rotation, latitudinal bands are studied based on intelligent computation methods. A combination of image fusion method with together tree decomposition is used to obtain quantitative values about the latitudes of trajectories on sun surface that sunspots rotate around them. Daily solar images taken with SOlar and Heliospheric (SOHO) satellite are fused for each month separately .The result of fused image is decomposed with Quad Tree decomposition method in order to achieve the precise information about latitudes of sunspot trajectories. Such analysis is useful for gathering information about the regions on sun surface and coordinates in space that is more expose to solar geomagnetic storms, tremendous flares and hot plasma gases permeate interplanetary space and help human to serve their technical systems. Here sunspot images in September, November and October in 2001 are used for studying the magnetic behavior of sun.

Keywords: Quad tree decomposition, sunspot image.

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: Day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring.

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Authors: Yi-Feng Su, Chia-Tseng Chen, Hsueh-Lung Liao

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The objective of this research is to develop an advanced driver assistance system characterized with the functions of lane departure warning (LDW), forward collision warning (FCW) and adaptive front-lighting system (AFS). The system is mainly configured a CCD/CMOS camera to acquire the images of roadway ahead in association with the analysis made by an image-processing unit concerning the lane ahead and the preceding vehicles. The input image captured by a camera is used to recognize the lane and the preceding vehicle positions by image detection and DROI (Dynamic Range of Interesting) algorithms. Therefore, the system is able to issue real-time auditory and visual outputs of warning when a driver is departing the lane or driving too close to approach the preceding vehicle unwittingly so that the danger could be prevented from occurring. During the nighttime, in addition to the foregoing warning functions, the system is able to control the bending light of headlamp to provide an immediate light illumination when making a turn at a curved lane and adjust the level automatically to reduce the lighting interference against the oncoming vehicles driving in the opposite direction by the curvature of lane and the vanishing point estimations. The experimental results show that the integrated vehicle image system is robust to most environments such as the lane detection and preceding vehicle detection average accuracy performances are both above 90 %.

Keywords: Lane mark detection, lane departure warning (LDW), dynamic range of interesting (DROI), forward collision warning (FCW), adaptive front-lighting system (AFS).

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: Facial expression recognition, image pre-processing, deep learning, CNN.

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Authors: Indradip Banerjee, Souvik Bhattacharyya, Gautam Sanyal

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‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.

Keywords: Steganalysis, Moments, Wavelet Domain, KNN, K*, LWL, Naive Bayes Classifier, Neural networks, Decision trees, SVM.

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: Computer vision, deep learning, object detection, semiconductor.

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Authors: H. Özkan

Abstract:

In this paper, a novel and fast algorithm for segmental and subsegmental lung vessel segmentation is introduced using Computed Tomography Angiography images. This process is quite important especially at the detection of pulmonary embolism, lung nodule, and interstitial lung disease. The applied method has been realized at five steps. At the first step, lung segmentation is achieved. At the second one, images are threshold and differences between the images are detected. At the third one, left and right lungs are gathered with the differences which are attained in the second step and Exact Lung Image (ELI) is achieved. At the fourth one, image, which is threshold for vessel, is gathered with the ELI. Lastly, identifying and segmentation of segmental and subsegmental lung vessel have been carried out thanks to image which is obtained in the fourth step. The performance of the applied method is found quite well for radiologists and it gives enough results to the surgeries medically.

Keywords: Computed tomography angiography (CTA), Computer aided detection (CAD), Lung segmentation, Lung vessel segmentation

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Authors: N. H. Harun, A. S. Abdul Nasir, M. Y. Mashor, R. Hassan

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Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year. There are two main categories for leukaemia, which are acute and chronic leukaemia. The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image. In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively. Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving kmeans clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.

Keywords: Acute Leukaemia Images, Clustering Algorithms, Image Segmentation, Moving k-Means.

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Authors: Dyah E. Herwindiati

Abstract:

A clustering is process to identify a homogeneous groups of object called as cluster. Clustering is one interesting topic on data mining. A group or class behaves similarly characteristics. This paper discusses a robust clustering process for data images with two reduction dimension approaches; i.e. the two dimensional principal component analysis (2DPCA) and principal component analysis (PCA). A standard approach to overcome this problem is dimension reduction, which transforms a high-dimensional data into a lower-dimensional space with limited loss of information. One of the most common forms of dimensionality reduction is the principal components analysis (PCA). The 2DPCA is often called a variant of principal component (PCA), the image matrices were directly treated as 2D matrices; they do not need to be transformed into a vector so that the covariance matrix of image can be constructed directly using the original image matrices. The decomposed classical covariance matrix is very sensitive to outlying observations. The objective of paper is to compare the performance of robust minimizing vector variance (MVV) in the two dimensional projection PCA (2DPCA) and the PCA for clustering on an arbitrary data image when outliers are hiden in the data set. The simulation aspects of robustness and the illustration of clustering images are discussed in the end of paper

Keywords: Breakdown point, Consistency, 2DPCA, PCA, Outlier, Vector Variance

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Authors: John N. Ellinas, Panagiotis Kenterlis

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The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The current paper presents an approach for still image digital watermarking in which the watermark embedding process employs the wavelet transform and incorporates Human Visual System (HVS) characteristics. The sensitivity of a human observer to contrast with respect to spatial frequency is described by the Contrast Sensitivity Function (CSF). The strength of the watermark within the decomposition subbands, which occupy an interval on the spatial frequencies, is adjusted according to this sensitivity. Moreover, the watermark embedding process is carried over the subband coefficients that lie on edges where distortions are less noticeable. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency.

Keywords: Image watermarking, wavelet transform, human visual system, contrast sensitivity function.

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Authors: Manasi Pathade, Prachi Kadam, Renuka Kulkarni, Tejas Teredesai

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In this paper, we propose a novel concept of relative distance measurement using Stereo Vision Technology and discuss its implementation on a FPGA based real-time image processor. We capture two images using two CCD cameras and compare them. Disparity is calculated for each pixel using a real time dense disparity calculation algorithm. This algorithm is based on the concept of indexed histogram for matching. Disparity being inversely proportional to distance (Proved Later), we can thus get the relative distances of objects in front of the camera. The output is displayed on a TV screen in the form of a depth image (optionally using pseudo colors). This system works in real time on a full PAL frame rate (720 x 576 active pixels @ 25 fps).

Keywords: Stereo Vision, Relative Distance Measurement, Indexed Histogram, Real time FPGA Image Processor

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Authors: Ching-Shoei Chiang, Sheng-Hsin Tsai, James Chen

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The PH curve can be constructed by given parameters, but the shape of the curve is not so easy to image from the value of the parameters. On the contract, Bézier curve can be constructed by the control polygon, and from the control polygon, we can image the figure of the curve. In this paper, we want to use the hodograph of Bézier curve to construct PH curve by selecting part of the control vectors, and produce other control vectors, so the property of PH curve exists.

Keywords: PH curve, hodograph, Bézier curve.

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Authors: S. Ratna Sulistiyanti, Adhi Susanto, Thomas Sri Widodo, Gede Bayu Suparta

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In this paper, an experimentation to enhance the visibility of hot objects in a thermal image acquired with ordinary digital camera is reported, after the applications of lowpass and median filters to suppress the distracting granular noises. The common thresholding and slicing techniques were used on the histogram at different gray levels, followed by a subjective comparative evaluation. The best result came out with the threshold level 115 and the number of slices 3.

Keywords: enhance, thermal image, thresholding and slicingtechniques, granular noise, hot objects.

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Authors: Meng-Hao Tsai, Tsuei-Chiang Huang

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Interior brick-infill partitions are usually considered as non-structural components, and only their weight is accounted for in practical structural design. In this study, the brick-infill panels are simulated by compression struts to clarify their effect on the progressive collapse potential of an earthquake-resistant RC building. Three-dimensional finite element models are constructed for the RC building subjected to sudden column loss. Linear static analyses are conducted to investigate the variation of demand-to-capacity ratio (DCR) of beam-end moment and the axial force variation of the beams adjacent to the removed column. Study results indicate that the brick-infill effect depends on their location with respect to the removed column. As they are filled in a structural bay with a shorter span adjacent to the column-removed line, more significant reduction of DCR may be achieved. However, under certain conditions, the brick infill may increase the axial tension of the two-span beam bridging the removed column.

Keywords: Progressive collapse, brick-infill partition, compression strut.

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Authors: Jaqueline M. R. Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge dataset configurations.

Keywords: Brazil, classifiers, data-mining, Image Segmentation, oil well visualization, classifiers.

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Authors: M. B. Meenavathi, K. Rajesh

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In this paper, we propose a new class of Volterra series based filters for image enhancement and restoration. Generally the linear filters reduce the noise and cause blurring at the edges. Some nonlinear filters based on median operator or rank operator deal with only impulse noise and fail to cancel the most common Gaussian distributed noise. A class of second order Volterra filters is proposed to optimize the trade-off between noise removal and edge preservation. In this paper, we consider both the Gaussian and mixed Gaussian-impulse noise to test the robustness of the filter. Image enhancement and restoration results using the proposed Volterra filter are found to be superior to those obtained with standard linear and nonlinear filters.

Keywords: Gaussian noise, Image enhancement, Imagerestoration, Linear filters, Nonlinear filters, Volterra series.

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Authors: Zhen-Hui Hu, Jyh-Shong Ju, Ming-Hwei Perng

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This paper presents a useful sub-pixel image registration method using line segments and a sub-pixel edge detector. In this approach, straight line segments are first extracted from gray images at the pixel level before applying the sub-pixel edge detector. Next, all sub-pixel line edges are mapped onto the orientation-distance parameter space to solve for line correspondence between images. Finally, the registration parameters with sub-pixel accuracy are analytically solved via two linear least-square problems. The present approach can be applied to various fields where fast registration with sub-pixel accuracy is required. To illustrate, the present approach is applied to the inspection of printed circuits on a flat panel. Numerical example shows that the present approach is effective and accurate when target images contain a sufficient number of line segments, which is true in many industrial problems.

Keywords: Defect detection, Image registration, Straight line segment, Sub-pixel.

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: Camera Calibration, Corner Detector, Saddle Points, X-Corners.

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Authors: M. Shaker, S. Minaei, M. H. Khoshtaghaza, A. Banakar, A. Jafari

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In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system.

Keywords: Computer vision, rice kernel, husking, breakage.

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Authors: Abhay Bodake, Ping Sui, Hafeez Syed, Ratish Kadam

Abstract:

Real-time measurement of applied forces, like tension, compression, torsion, and bending moment, identifies the transferred energies being applied to the bottomhole assembly (BHA). These forces are highly detrimental to measurement/logging-while-drilling tools and downhole equipment. Real-time measurement of the dynamic downhole behavior, including weight, torque, bending on bit, and vibration, establishes a real-time feedback loop between the downhole drilling system and drilling team at the surface. This paper describes the numerical analysis of the strain data acquired by the measurement tool at different locations on the strain pockets. The strain values obtained by FEA for various loading conditions (tension, compression, torque, and bending moment) are compared against experimental results obtained from an identical experimental setup. Numerical analyses results agree with experimental data within 8% and, therefore, substantiate and validate the FEA model. This FEA model can be used to analyze the combined loading conditions that reflect the actual drilling environment.

Keywords: FEA, M/LWD, Oil & Gas, Strain Measurement.

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Authors: Seda Yavuz, Anıl Çelebi, Aysun Taşyapı Çelebi, Oğuzhan Urhan

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Nowadays, demand for using real-time video transmission capable devices is ever-increasing. So, high resolution videos have made efficient video compression techniques an essential component for capturing and transmitting video data. Motion estimation has a critical role in encoding raw video. Hence, various motion estimation methods are introduced to efficiently compress the video. Low bit‑depth representation based motion estimation methods facilitate computation of matching criteria and thus, provide small hardware footprint. In this paper, a hardware implementation of a two-bit transformation based low-complexity motion estimation method using local binary pattern approach is proposed. Image frames are represented in two-bit depth instead of full-depth by making use of the local binary pattern as a binarization approach and the binarization part of the hardware architecture is explained in detail. Experimental results demonstrate the difference between the proposed hardware architecture and the architectures of well-known low-complexity motion estimation methods in terms of important aspects such as resource utilization, energy and power consumption.

Keywords: Binarization, hardware architecture, local binary pattern, motion estimation, two-bit transform.

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Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

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In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.

Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.

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Authors: Wei-Der Chang, Dai-Ming Chang, Eri-Wei Chiang, Chia-Hung Lin, Jian-Liung Chen

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In this paper, a fractional-order FIR differentiator design method using the differential evolution (DE) algorithm is presented. In the proposed method, the FIR digital filter is designed to meet the frequency response of a desired fractal-order differentiator, which is evaluated in the frequency domain. To verify the design performance, another design method considered in the time-domain is also provided. Simulation results reveal the efficiency of the proposed method.

Keywords: Fractional-order differentiator, FIR digital filter, Differential evolution algorithm.

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Authors: J. H. Zhou, C. S. Cheung, C. W. Leung

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The present study conducted experimental investigation on combustion and emission characteristics of compression ignition engine using diesel as pilot fuel and methane, hydrogen and methane/hydrogen mixture as gaseous fuels at 1800 rev min-1. The effect of gaseous fuel on peak cylinder pressure and heat release is modest at low to medium loads. At high load, the high combustion temperature and high quantity of pilot fuel contribute to better combustion efficiency for all kinds of gaseous fuels and increases the peak cylinder pressure. Enrichment of hydrogen in methane gradually increases the peak cylinder pressure. The brake thermal efficiency increases with higher hydrogen fraction at lower loads. Hydrogen addition in methane contributed to a proportional reduction of CO/CO2/HC emission without penalty of NOx. For particulate emission, methane and hydrogen, could both suppress the particle emission. 30% hydrogen fraction in methane is observed to be best in reducing the particulate emission.

Keywords: Combustion characteristics, diesel engine, emissions, methane/hydrogen mixture.

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

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In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

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In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: Dropwise condensation, textured surface, image processing, watershed.

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