Figure 1: Undesirable artifacts in photography can be reduced by comparing image gradients at corresponding locations in a pair of flash and ambient images. (Left) Removing flash hot spot. Flash and ambient images of a museum scene, where the flash image reveals more of the scene but includes a strong highlight. We combine gradients in flash and ambient images to produce an enhanced flash image with the highlight removed. (Right) Removing self reflections. Flash and ambient images of a painting, where the ambient image includes annoying reflections of the photographer. The low-exposure flash image avoids reflections, but has a hot spot. We remove the reflections in the ambient image by removing the component of the ambient image gradients perpendicular to the flash image gradients. For visual verification, we show the computed reflection layer. Flash images are known to suffer from several problems: saturation of nearby objects, poor illumination of distant objects, reflections of objects strongly lit by the flash and strong highlights due to the reflection of flash itself by glossy surfaces. We propose to use a flash and no-flash (ambient) image pair to produce better flash images. We present a novel gradient projection scheme based on a gradient coherence model that allows removal of reflections and

Abstract—Based on the analysis of temporal slices, we propose novel approaches for clustering and retrieval of video shots. Temporal slices are a set of two–dimensional (2-D) images extracted along the time dimension of an image volume. They encode rich set of visual patterns for similarity measure. In this paper, we first demonstrate that tensor histogram features extracted from temporal slices are suitable for motion retrieval. Subsequently, we integrate both tensor and color histograms for constructing a two-level hierarchical clustering structure. Each cluster in the top level contains shots with similar color while each cluster in bottom level consists of shots with similar motion. The constructed structure is then used for the cluster-based retrieval. The proposed approaches are found to be useful particularly for sports games, where motion and color are important visual cues when searching and browsing the desired video shots. Index Terms—Hierarchical clustering, motion retrieval, temporal slices, tensor histogram. I.

A scheme for performing generalized convolutions is presented. A flexible convolver, which runs on standard workstations, has been implemented. It is designed for maximum throughput and flexibility. The implementation incorporates spatio-temporal convolutions with configurable vector combinations. It can handle general multi-linear operations, i.e. tensor operations on multidimensional data of any order. The input data and the kernel coefficients can be of arbitrary vector length. The convolver is configurable for IIR filters in the time dimension. Other features of the implemented convolver are scattered kernel data, region of interest and subsampling. The implementation is done as a C-library and a graphical user interface in AVS (Application Visualization System). 1.

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This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. In this paper, we first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of di#erent moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent sub-units, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.

We propose a new technique for edge-suppressing operations on images. We introduce cross projection tensors to achieve affine transformations of gradient fields. We use these tensors, for example, to remove edges in one image based on the edge-information in a second image. Traditionally, edge suppression is achieved by setting image gradients to zero based on thresholds. A common application is in the Retinex problem, where the illumination map is recovered by suppressing the reflectance edges, assuming it is slowly varying.

This paper presents a novel approach for estimating the flow fields of dynamic temporal textures whose motion differs radically from that of rigid bodies.

Abstract. Junctions form critical features in motion segmentation, image enhancement, and object classification to name but a few application domains. Traditional approaches to identifying junctions include convolutional methods, which involve considerable tuning to handle non-trivial inputs and diffusion techniques that address only symmetric structure. A new approach is proposed that requires minimal tuning and can distinguish between the basic, but critically different, ‘X ’ and ‘T ’ junctions. This involves a multi-directional representation of gradient structure and employs asymmetric tensor diffusion to emphasize such junctions. The approach combines the desirable properties of asymmetry from convolutional methods with the robustness of local support from diffusion. 1

Abstract—Optical flow algorithms are difficult to apply to robotic vision applications in practice because of their extremely high computational and frame rate requirements. In most cases, traditional general purpose processors and sequentially executed software cannot compute optical flow in real time. In this paper, a tensor-based optical flow algorithm is developed and implemented using field programmable gate array (FPGA) technology. The resulting algorithm is significantly more accurate than previously published FPGA results and was specifically developed to be implemented using a pipelined hardware structure. The design can process 640 × 480 images at 64 fps, which is fast enough for most real-time robot navigation applications. This design has low resource requirements, making it easier to fit into small embedded systems. Error analysis on a synthetic image sequence is given to show its effectiveness. The algorithm is also tested on a real image sequence to show its robustness and limitations. The resulting limitations are analyzed and an improved scheme is then proposed. It is then shown that the performance of the design could be substantially improved with sufficient hardware resources.

Abstract — We propose an approach to classify the film classes by using low level features and visual features. This approach aims to classify the films into genres. Our current domain of study is using the movie preview. A movie preview often emphasizes the theme of a film and hence provides suitable information for classifying process. In our approach, we categorize films into three broad categories: action, dramas, and thriller films. Four computable video features (average shot length, color variance, motion content and lighting key) and visual features (show and fast moving effects) are combined in our approach to provide the advantage information to demonstrate the movie category. The experimental results present that visual features are the useful messages for processing the film classification. On the other hand, our approach can also be extended for other potential applications, including the browsing and retrieval of videos on the internet, video-on-demand, and video libraries. Index Terms — Film classifier, movie genre, shot boundary detection, visual feature I.