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This paper reviews the concept of straight skeletons, which is well known in computational geometry, and applies it to binary shapes that are used in vision-based shape and object recognition. We devise a novel algorithm for computing discrete straight skeletons from binary input images, which is based on a polygonal approximation of the input shape and a hybrid method that combines continuous and...
Detecting an object part relies on two sources of information - the appearance of the part itself and the context supplied by surrounding parts. In this paper we consider problems in which a target part cannot be recognized reliably using its own appearance, such as detecting low-resolution hands, and must be recognized using the context of surrounding parts. We develop the `chains model' which can...
Human identity recognition is an important yet under-addressed problem. Previous methods were strictly limited to high quality photographs, where the principal techniques heavily rely on body details such as face detection. In this paper, we propose an algorithm to address the novel problem of human identity recognition over a set of unordered low quality aerial images. Assuming a user was able to...
It has been demonstrated that it is possible to reconstruct 3D video sequences using depth information. Novel framework can be realized in all formats in video sequences because it does not depend on a specific hardware usually needed in existed techniques for depth map calculations. Proposed technique is applicable in other methods of 3D perception, such as polarized lens, etc. The applications of...
In this paper we present a system for mobile augmented reality (AR) based on visual recognition. We split the tasks of recognizing an object and tracking it on the user's screen into a server-side and a client-side task, respectively. The capabilities of this hybrid client-server approach are demonstrated with a prototype application on the Android platform, which is able to augment both stationary...
The present paper addresses the problem of image segmentation evaluation by comparing four different approaches. We are introducing a new method of salient object recognition with very good results relative to other already known object detection methods. We developed a simple evaluation framework in order to compare the results of our method with other segmentation methods. The experimental results...
Feature-based methods have found increasing use in many applications such as object recognition, 3D reconstruction and mosaicing. In this paper, we focus on the problem of matching such features. While a histogram-of-gradients type methods such as SIFT, GLOH and Shape Context are currently popular, several papers have suggested using orders of pixels rather than raw intensities and shown improved...
We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more...
We present a contour based approach to object recognition in real-world images. Contours are represented by generic shape primitives of line segments and ellipses. These primitives offer substantial flexibility to model complex shapes. We pair connected primitives as shape tokens, and learn category specific combinations of shape tokens. We do not restrict combinations to have a fixed number of tokens,...
We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of only image level labels during training - the information whether a certain object is present or not in the image. Such coarse tagging of images is faster and easier to obtain as opposed to the tedious task of pixelwise labeling...
Purely bottom-up, unsupervised segmentation of a single image into foreground and background regions remains a challenging task for computer vision. Co-segmentation is the problem of simultaneously dividing multiple images into regions (segments) corresponding to different object classes. In this paper, we combine existing tools for bottom-up image segmentation such as normalized cuts, with kernel...
Identifying handled objects, i.e. objects being manipulated by a user, is essential for recognizing the person's activities. An egocentric camera as worn on the body enjoys many advantages such as having a natural first-person view and not needing to instrument the environment. It is also a challenging setting, where background clutter is known to be a major source of problems and is difficult to...
Local invariant features have been widely used as fundamental elements for image matching and object recognition. Although dense sampling of local features is useful in achieving an improved performance in image matching and object recognition, it results in increased computational costs for feature extraction. The purpose of this paper is to develop fast computational techniques for extracting local...
We propose a new method for video retargeting, which can generate spatial-temporal consistent video. The new measure called spatial-temporal naturality preserves the motion in the source video without any motion analysis in contrast to other methods that need motion estimation. This advantage prevents the retargeted video from degenerating due to the propagation of the errors in motion analysis. It...
This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural...
Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft...
We present an approach to visual object-class recognition and segmentation based on a pipeline that combines multiple, holistic figure-ground hypotheses generated in a bottom-up, object independent process. Decisions are performed based on continuous estimates of the spatial overlap between image segment hypotheses and each putative class. We differ from existing approaches not only in our seemingly...
Empirical evaluation of salient object segmentation methods requires i) a dataset of ground truth object segmentations and ii) a performance measure to compare the output of the algorithm with the ground truth. In this paper, we provide such a dataset, and evaluate 5 distinct performance measures that have been used in the literature practically and psychophysically. Our results suggest that a measure...
Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors. In this paper we explore global self-similarity (GSS) and its advantages over local self-similarity (LSS). We make three contributions: (a) we propose computationally efficient algorithms to extract GSS descriptors for classification. These capture...
Automatic detection of road sign is a challenging but demanding job. A new approach namely automatic detection and recognition of traffic signs (ADRTS) considering color segmentation, moment invariants, and neural networks has been proposed in this paper. Experimental result proves the superior performance in the detection and recognition of road signs. Computational time complexity is also quite...
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