Proceedings. IEEE Workshop on Applications of Computer Vision最新文献

The current trend in high throughput screening is the utilization of more complex model systems that mimic both structural and functional properties of cellular processes in vivo. In this context, 3D cell culture models have emerged as effective systems to study tumor initiation and cancer behavior, where colony organization represents distinct phenotypic signatures that enable differentiation of cancer cells in culture using phase imaging and in the absence of clinical markers. If the colony organization can be classified into different phenotypes, it will enable rapid drug screening using phase microscopy. In this paper, we propose a novel method based on locality-constrained dictionary learning for the discrimination of aberrant colony organization in phase images, which encodes original SIFT (Scale-Invariant Feature Transform) features into high dimensional sparse codes with locality-preserving landmark points on the nonlinear manifold, and summarizes the sparse features at various locations and scales through spatial pyramid matching for robust representation. Experimental results demonstrate the significant improvement of performance, compared to the state-of-art in the field.

This paper investigates the problem of image alignment for multiple camera high dynamic range (HDR) imaging. HDR imaging combines information from images taken with different exposure settings. Combining information from multiple cameras requires an alignment process that is robust to the intensity differences in the images. HDR applications that use a limited number of component images require an alignment technique that is robust to large exposure differences. We evaluate the suitability for HDR alignment of three exposure-robust techniques. We conclude that image alignment based on matching feature descriptors extracted from radiant power images from calibrated cameras yields the most accurate and robust solution. We demonstrate the use of this alignment technique in a high dynamic range video microscope that enables live specimen imaging with a greater level of detail than can be captured with a single camera.

There is a growing body of work addressing the problem of localizing printed text regions occurring in natural scenes, all of it focused on images in which the text to be localized is resolved clearly enough to be read by OCR. This paper introduces an alternative approach to text localization based on the fact that it is often useful to localize text that is identifiable as text but too blurry or small to be read, for two reasons. First, an image can be decimated and processed at a coarser resolution than usual, resulting in faster localization before OCR is performed (at full resolution, if needed). Second, in real-time applications such as a cell phone app to find and read text, text may initially be acquired from a lower-resolution video image in which it appears too small to be read; once the text's presence and location have been established, a higher-resolution image can be taken in order to resolve the text clearly enough to read it.We demonstrate proof of concept of this approach by describing a novel algorithm for binarizing the image and extracting candidate text features, called "blobs," and grouping and classifying the blobs into text and non-text categories. Experimental results are shown on a variety of images in which the text is resolved too poorly to be clearly read, but is still identifiable by our algorithm as text.

Modern household appliances, such as microwave ovens and DVD players, increasingly require users to read an LED or LCD display to operate them, posing a severe obstacle for persons with blindness or visual impairment. While OCR-enabled devices are emerging to address the related problem of reading text in printed documents, they are not designed to tackle the challenge of finding and reading characters in appliance displays. Any system for reading these characters must address the challenge of first locating the characters among substantial amounts of background clutter; moreover, poor contrast and the abundance of specular highlights on the display surface - which degrade the image in an unpredictable way as the camera is moved - motivate the need for a system that processes images at a few frames per second, rather than forcing the user to take several photos, each of which can take seconds to acquire and process, until one is readable.We describe a novel system that acquires video, detects and reads LED/LCD characters in real time, reading them aloud to the user with synthesized speech. The system has been implemented on both a desktop and a cell phone. Experimental results are reported on videos of display images, demonstrating the feasibility of the system.

A general framework is proposed for solving groupwise pose normalization problems and is analyzed in detail under different feature spaces. The analysis shows that using principal component analysis for pose normalization is a special case of using the proposed framework under a special feature space. The experimental results on two craniofacial datasets show the proposed method achieved promising results for solving groupwise pose normalization problems for craniofacial applications.

Most camera-based systems for finding and reading barcodes are designed to be used by sighted users (e.g. the Red Laser iPhone app), and assume the user carefully centers the barcode in the image before the barcode is read. Blind individuals could benefit greatly from such systems to identify packaged goods (such as canned goods in a supermarket), but unfortunately in their current form these systems are completely inaccessible because of their reliance on visual feedback from the user.To remedy this problem, we propose a computer vision algorithm that processes several frames of video per second to detect barcodes from a distance of several inches; the algorithm issues directional information with audio feedback (e.g. "left," "right") and thereby guides a blind user holding a webcam or other portable camera to locate and home in on a barcode. Once the barcode is detected at sufficiently close range, a barcode reading algorithm previously developed by the authors scans and reads aloud the barcode and the corresponding product information. We demonstrate encouraging experimental results of our proposed system implemented on a desktop computer with a webcam held by a blindfolded user; ultimately the system will be ported to a camera phone for use by visually impaired users.

Current camera-based barcode readers do not work well when the image has low resolution, is out of focus, or is motion-blurred. One main reason is that virtually all existing algorithms perform some sort of binarization, either by gray scale thresholding or by finding the bar edges. We propose a new approach to barcode reading that never needs to binarize the image. Instead, we use deformable barcode digit models in a maximum likelihood setting. We show that the particular nature of these models enables efficient integration over the space of deformations. Global optimization over all digits is then performed using dynamic programming. Experiments with challenging UPC-A barcode images show substantial improvement over other state-of-the-art algorithms.