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Nowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In...
Digital image processing techniques are commonly employed for food classification in an industrial environment. In this paper, we propose the use of supervised learning methods, namely multi-class support vector machines and artificial neural networks to perform classification of different type of almonds. In the process of defining the feature vectors, the proposed method has relied on the principal...
Scene analysis is an integral part of autonomous vehicle. Information such as traffic sign boards information, Toll information, Vehicle Number Plate information etc., from the scene is helpful in analyzing the current location. Vision based techniques are critical for a given scene analysis and happens to be an important area of research, today. This paper present localizing and recognizing text...
Action recognition has been an active research area in computer vision community during the recent years. However, it is still a challenging task due to the difficulties mainly resulted from the background clutter, illumination changes, large intra-class variation and noise. In this paper, we aim to develop an action recognition approach by navigating focus of attention (action region) with saliency...
Images on the Internet and in multimedia systems are rising successively. There are different research works on visual information and automatic analysis of images. Image memorability is a new task in computer vision. Actually, the human brain processes simultaneously millions of images and other information from multiple sources. Among these various images and information some of them are more memorable...
Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning...
This paper presents an ensemble-SVM method that features a data selection mechanism with stochastic and deterministic properties, the use of extreme value theory for classifier calibration, and the introduction of random forest for classifier combination. We applied the proposed algorithm to 2 event recognition datasets and the PASCAL2007 object detection dataset and compared it to single SVM and...
The paper proposes a mobile application for clothing coordination, which could be of great benefit for stores and people seek for fashion advices. The application matches apparel image input with, previously saved apparel images, and then provides the user with the possible matching suggestions based on the apparel outline and dominating colors. For this purpose two Region of Interest (ROI) extraction...
Biomedical in vivo imaging has been playing an essential role in diagnoses and treatment in modern medicine. However, compared with the fast development of medical imaging systems, the medical imaging informatics, especially automated prediction, has not been fully explored. In our paper, we compared different feature extraction and classification methods for prediction pipeline to analyze in vivo...
Due to the influence of both background interference and time-varying position, it is difficult to detect the moving object by computer vision in the complex background environment. This paper novelly builds a topological structure of typical features to detect object. The presented method can effectively detect the object which is scaling, rotating or in affine transformation, because of the using...
In this paper, we present a motorcycle detection system in static images leading to its application in crash avoidance systems. Motorcycles are common mode of transport in ASEAN countries and contribute more road crashes than any other mode of transport. In our proposed system, motorbikes are detected based on the helmet and tyre color characteristics. This method involves the fusion of shape, color...
The detection of early manmade fire carries profound meaning in warning systems to prevent fire-related terrorist attacks. Despite a large number of work on fire detection in the computer vision literature, there is no specific method for early stage manmade fire detection, our best knowledge. Compared to traditional fire detection, there is less information on early stage man-made fire for detection,...
Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are...
In this paper, we propose a supervised approach to find out the probabilistic mapping of semantic contours in color images. We prepare a new color image modifying the RGB color planes to incorporate reasonable within-object contrasts in all the color planes. Color gradient based features are then extracted from this altered version of color image. Next, multiple support vector machines (SVMs) are...
This paper describes the initial design of a computer vision application to recognize regulatory traffic signs vertically installed on Colombian roads using machine learning. This application is conceived as a module of a driver assistance system under development, and an autonomous vehicle adapted to the local infrastructure. The application was trained and tested with official synthetic images provided...
We present a plant image recognition system geared towards plants with flowers. The system uses local invariants with Dense SIFT features and Bag of Visual Words representation, while the classification is done using Support Vector Machines. Our approach contains a pre-classification stage where images are categorized into color subgroups, to reduce the complexity of the problem. Using a 161-class...
Skin detection is the preliminary stage of many computer vision applications. In this paper, a statistical fusion model is proposed for detecting skin regions in arbitrary images. We used conditional random field (CRF) to statistically combine the information of different color spaces and model the spatial relationship between image pixels. The conditional probability distribution of labels (skin...
Texture classification is an important part of many object recognition algorithms. In this paper, a new approach to texture classification is proposed. Recently, local binary pattern (LBP) has been widely used in texture classification. In conventional LBP, directional statistical features and color information are not considered. To extract color information of textures, we have used color LBP. Also,...
Human action recognition is the process of labeling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around...
The extraction of nuclei from Haematoxylin and Eosin (H&E) stained biopsies present a particularly steep challenge in part due to the irregularity of the high-grade (most malignant) tumors. To your best knowledge, although some existing solutions perform adequately with relatively predictable low-grade cancers, solutions for the problematic high-grade cancers have yet to be proposed. In this...
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