Machine Vision

This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior model with respect to sparsity, reconstruction error and interpretability is found to be a varimax rotated model with a threshold applied to small loadings. The models describe the biological variation in the database and are used for developing robotic tools when automating labor-intensive procedures in abattoirs.

A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.

This paper presents algorithms based on image processing for detecting internal blood spots and eggshell dirt by processing acquired images from eggs under different illuminations. The algorithm can also detect the severity of dirt on eggshell. In order to carry out image processing and extract useful features of captured images of eggs by machine vision we developed an algorithm in

Computer Vision is applied in agriculture field for food grading, disease identification of the plants and agro-farms security. Huge crop damage is caused by the wild animal attacks on the agriculture farms. Here are some traditional techniques followed by the local farmers, but which are not effective. This problem can be solved using computer vision techniques. In this paper, we proposed an algorithm to detect animals in a given image. W-CoHOG is a Histogram oriented gradients based feature vector with better accuracy. It is an extension of Co-occurrence Histograms of Oriented Gradients (CoHOG). In this paper LIBLINEAR classifier is used in order to get better accuracy for high dimensional data. The experiments were conducted on two benchmark datasets called Wild-Anim and CamaraTrap dataset. Experimental results prove that W-CoHOG performs better than existing state of the art methods

An approach to active vision based on information theory and statistical mechanics is presented. Density of entropy production measured along a spatio-chromatic diffusion of a colour image is used to build a conspicuity map of the image. The map is successively given as input to a dynamic neural network in order to drive a focus-of-attention scanpath

Intelligent Vehicles, as a main part of Intelligent Transportation Systems (ITS), will have great impact on transportation in near future. They would be able to understand their immediate environment and also communicate with other traffic participants such as other vehicles, infrastructures and traffic management centres. Intelligent vehicles could navigate autonomously in highway and urban scenarios using maps, GPS, video sensors and so on. To navigate autonomously or follow a road, intelligent vehicles need to detect lanes. It seems that the best cue for lane detection is to use the lane markings painted on roads and it should be noticed that among passive and active sensors, the video sensors are the best candidate for finding lane markings. In this paper we present a method for lane detection in image sequences of a camera mounted behind the windshield of a vehicle. The main idea is to find the features of the lane in consecutive frames which match a particular geometric model....

Abstract. In RoboCup Middle Size league (MSL) the main referee uses assisting technology, controlled by a second referee, to support him, in particular for conveying referee decisions for robot players with the help of a wireless communication system. In this paper a vision-based system is introduced, able to interpret dynamic and static gestures of the referee, thus eliminating the need for a second one. The referee's ges-tures are interpreted by the system and sent directly to the Referee Box, which sends the proper commands to the robots. The system is divided into four modules: a real time hand tracking and feature extraction, a SVM (Support Vector Machine) for static hand posture identification, an HMM (Hidden Markov Model) for dynamic unistroke hand gesture recognition, and a FSM (Finite State Machine) to control the various system states transitions. The experimental results showed that the system works very reliably, being able to recognize the combination of gestures a...

In this paper, we propose a camera based novel, real-time framework for detection and tracking of vulnerable road users, such as pedestrians and cyclists. Our framework also gives a measure of the degree of vulnerability based on the direction of movement and distance from the vulnerable region. Pedestrians and cyclists are the most vulnerable road users and it is necessary to develop automated systems that can detect them and ensure their safety by alerting the driver. In our framework, we apply deep learning based method for 2D pose detection for detecting the pedestrians and cyclists in the view of the outside looking camera mounted on the dashboard of a vehicle. As the vehicle moves, the pedestrians and cyclists are detected and tracked across frames, their degree of vulnerability is measured and the driver is alerted in case of high vulnerability score. Experimental results show that the our framework is able to accurately detect vulnerable road users and measure their degree of vulnerability.

Corner detection is widely used in image processing and machine vision. Hence, different corner detectors are proposed. But the performance of such corner detectors is sensitive to round effect and curve shape of the edges. In this paper we propose a novel corner detector that either over come corner detection problems and produces some information about detected corners that is very useful in segmentation and object recognition.

Nondestructive optical methods based on image analysis have been used for determining quality of tomato fruit. It is rapid and requires less sample preparation. A samples of fresh tomatoes were picked at different maturity stages, and determining chromaticity values (L*,a*,b*,a*/b*,h˚and ΔE) by image analysis and colorimeter. Total soluble solids (TSS), were measured by refractometer, lycopene extracting and expressed as mg/kg fresh tomato (FW). Results indicated that, during ripening both L*, b*, h˚, and ΔE tendency to decline, opposite tendency was determined with a*, a*/b* ratio, TSS and lycopene content. Chromaticity values have an important impact in internal quality parameters. Where, avg. of TSS, entire class and lycopene content had a positive linear correlation with a*/b* ratio. Contrary correlation was determined between avg. of TSS, entire class and both h˚ and ΔE. Meanwhile, h˚ and ΔE, had a negative logarithmic correlation with lycopene content. On the other hand, there...

Face and facial pattern detection is an active research area that is useful for many practical applications. In this paper, we present an innovative technique for eye detection using Augmented Dynamic Time Warping (ADTW) algorithm. Dynamic Time Warping (DTW) is a reliable algorithm that can perform similarity matching between two sequences but it requires supplimnetry techniques to be effective and efficient when used as a pattern detection tool. Therefore, for template matching in a facial pattern detection, the DTW is augmented with proposed image processing strategy along with a proper weighting scheme to increase its efficiency and accuracy. Our proposed ADTW overcomes some of the shortcomings associated with the traditional dynamic time warping and hence improves the alignment of the warping path of the sequences. The results of the implementation of the ADTW to detect eyes in facial images of subject with and without glasses show a marked improvement in detection accuracy when compared with that of the traditional DTW. © 2012 ICIC International.

www.ijicic.org/10-08069-1.pdf

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Edge detection is one of the most important tasks in machine vision and image processing. The paper presents novel approach to construction of algorithm for detection edges in images. In order to solve the problem the concepts of the fuzzy similarity relation and homogeneity region were exploited. The results of application of the proposed edge calculation method are presented in this paper. Additionally, this paper presents an interesting artistic effect obtained by application of the image processing method based on such the detection of edges. Possible artistic effects of application of this algorithm are exemplified.

The automatic classification of defective eggs constitutes a fundamental issue at the poultry industry for both economical and sanitary reasons. The early separation of eggs with spots and cracks is a relevant task as the stains can leak while progressing on the conveyor-belts, degrading all the mechanical parts. Present work is focused on the implementation of an artificial vision system for detecting in real time defective eggs at the poultry farm. First step of the algorithmic process is devoted to the detection of the egg shape to fix the region of interest. A color processing is then performed only on the eggshell to obtain an image segmentation that allows the discrimination of defective eggs from clean ones in critic time. The results are presented to demonstrate the validity of the proposed visual process on a wide sample of both defective and non-defective eggs.

ABSTRAK Dalam penelitian ini dilakukan sebuah perancangan algoritma identifikasi kabel menggunakan metode image processing. Perancangan algoritma dilakukan menggunakan software Matlab dan akusisi citra menggunakan bantuak software Microsoft Office 2010. Kontribusi yang dilakukan dalam penelitian ini adalah merancang algoritma perhitungan kabel dan penunjukan bagian kesalahan yang terjadi pada susunan kabel dalam citra. Untuk mengetahui performa dari algoritma yang dibuat dilakukan uji coba pada 10 gambar yang telah dibuat. Didapatkan juga waktu pemrosesan rata rata untuk dijadikan parameter ketika di aplikasikan lebih lanjut. Algoritma dapat berjalan dengan baik dimana memiliki presentasi akurasi rata – rata sebesar 96.7 % dan waktu pemrosesan rata – rata sebesar 1.8079 sekon. Citra dengan jumlah kabel sebanyak 22 buah di dalamnya memiliki tingkat akurasi tertinggi dibandingkan dengan jumlah kabel lainnya.