Learn More
Classic <i>Content-Based Image Retrieval</i> (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define <i>Localized Content-Based Image(More)
Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular(More)
Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent(More)
We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training(More)
Region-based image segmentation is one popular approach to image segmentation of generic images. In these methods, an image is partitioned into connected regions by grouping neighboring pixels of similar features, and adjacent regions are then merged with respect to the similarities between the features in these regions. To achieve fine-grain segmentation(More)
Object segmentation is an important preprocessing step for many target recognition applications. Many seg-mentation methods have been studied, but there is still no satisfactory effectiveness measure which makes it hard to compare different segmentation methods, or even different parameterizations of a single method. A good segmentation evaluation method(More)
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods(More)
Applications for constrained embedded systems require careful attention to the match between the application and the support offered by an architecture, at the ISA and microarchitecture levels. Generic processors, such as ARM and Power PC, are inexpensive, but with respect to a given application, they often overprovision in areas that are unimportant for(More)
We present the design, implementation, and evaluation of a circuit we call the Statistics Module that captures cycle-accurate performance data at (or above) the microarchitecture layer. The circuit is deployed introspectively—in the architecture itself— using an FPGA in the context of a soft-core implementation of a SPARC architecture (LEON). Accessible(More)