Object Edge Contour Localisation Based on HexBinary Feature Matching

This paper addresses the issue of localising object edge contours in cluttered backgrounds to support robotics tasks such as grasping and manipulation and also to improve the potential perceptual capabilities of robot vision systems. Our approach is based on coarse-to-fine matching of a new recursively constructed hierarchical, dense, edge-localised descriptor, the HexBinary, based on the HexHog descriptor structure first proposed in [1]. Since Binary String image descriptors [2]– [5] require much lower computational resources, but provide similar or even better matching performance than Histogram of Orientated Gradient (HoG) descriptors, we have replaced the HoG base descriptor fields used in HexHog with Binary Strings generated from first and second order polar derivative approximations. The ALOI [6] dataset is used to evaluate the HexBinary descriptors which we demonstrate to achieve a superior performance to that of HexHoG [1] for pose refinement. The validation of our object contour localisation system shows promising results with correctly labelling ~86% of edgel positions and mis-labelling ~3%

Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Measurement of Mid-span Deflections by using Digital Image Processing

Nowadays, several measurement techniques and devices have been developed to measure the deformations on structural members, such as gages, low voltage displacement transducers (LVDT), strain gages and acoustic emissions (AE). With the recent developments, the photography is now in use of science to measure the deformations via the Digital Image Processing (DIP). This research aimed to determine and compare the mid-span deflections of wooden beams that are subjected to three-point bending load. The mid-span deflections of wooden frames are collected via the comparators and DIP. The behavior of the wooden beam is also compared with numerical results generated by using finite element method (FEM). It is observed that the DIP is considerable while measuring the deformations of structural members. Keywords: digital image processing, wooden beam, three point bending load, mid-span deflection, FE

CLIPS and NII at TRECvid: Shot segmentation and feature extraction

International audienceThis paper presents the systems used by CLIPS- IMAG laboratory. We participated to shot seg- mentation and high-level extraction tasks. We fo- cus this year on High-Level Features Extraction task, based on key frames classification. We pro- pose an original and promising framework for in- corporating contextual information (from image content) into the concept detection process. The proposed method combines local and global clas- sifiers with stacking, using SVM. We handle topo- logic and semantic contexts in concept detection performance and proposed solutions to handle the large amount of dimensions involved in classified data

Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

Using the Forest to See the Trees: Exploiting Context for Visual Object Detection and Localization

Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be; this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems.United States. National Geospatial-Intelligence Agency (NEGI-1582-04-0004)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant Number N00014-06-1-0734)National Science Foundation (U.S.). (Contract IIS-0413232)National Defense Science and Engineering Graduate Fellowshi

A Comparative Study of Features Extracted in the Classification of Human Skin Burn Depth

The first burn treatment provided to patient is usually based on the first evaluation of the skin burn injury by determining the burn depths. In this paper, the objective is to conduct a comparative study of the different set of features extracted and used in the classification of different burn depths by using an image mining approach. Seven sets of global features and 5 local feature descriptors were studied on a skin burn dataset comprising skin burn images categorized into three burn classes by medical experts. The performance of the studied global and local features were evaluated using SMO, JRIP, and J48 on 10-fold cross validation method. The empirical results showed that the best set of features that was able to classify most of the burn depths consisted of mean of lightness, mean of hue, standard deviation of hue, standard deviation of A* component, standard deviation of B* component, and skewness of lightness with an average accuracy of 77.0% whereas the best descriptor in terms of local features for skin burn images was SIFT, with an average accuracy of 74.7%. It can be concluded that a combination of global and local features is able to provide sufficient information for the classification of the skin burn depths

OBJECT THEFT DETECTION SYSTEM

Often, in our daily life, we sometimes do not notice our belongings which we put it on the table or on the walls .are taken or stolen by somebody. Jn a picture gallery for example, video surveillance are very essential to monitor the arts and pictures which are exposed for viewing to the public. However, human operators often lose his focuses if he needs to monitor the videos, which may probably range from one to more than one, in a long hour. Therefore, when a ste_eling scenes occ.urs,they may not be alerted in an instance. Thus, a semiautomatic tools or system to alert the human operator when this type of event occurs may become a helpful method to assistthe human operators in performing their job efficiently and effectively. By using object recognition techniques together with video image processing tools available in MATL;\B®, this paper discussed the implementation of the available methods to come oljr with Ill'\ Object T!u;ft Detection System which can detect stolen object in real-time environment. and issue an alarm when emergency occurs, li