Robert Laganière

Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.

Detecting planar homographies in an image pair

Because of their abundance and simplicity, planes are used in several computer vision tasks. Their simplicity results in that, under perspective projection, the transformation between a world plane and its corresponding image plane is projective linear, or a homography. These relations also hold between perspective views of a plane in different images. This paper proposes an algorithm that detects planar homographies in uncalibrated image pairs. It then demonstrates how this plane identification method can be used as a first step in an image analysis process, when point matching between images is unreliable. The detection is performed using a RANSAC scheme based on the linear computation of the homography matrix elements using four points. Results are shown on real image pairs.

Sampling Strategies for Real-Time Action Recognition

Local spatio-temporal features and bag-of-features representations have become popular for action recognition. A recent trend is to use dense sampling for better performance. While many methods claimed to use dense feature sets, most of them are just denser than approaches based on sparse interest point detectors. In this paper, we explore sampling with high density on action recognition. We also investigate the impact of random sampling over dense grid for computational efficiency. We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model. A new method based on histogram intersection kernel is proposed to combine multiple channels of different descriptors. Our technique shows high accuracy on the simple KTH dataset, and achieves state-of-the-art on two very challenging real-world datasets, namely, 93% on KTH, 83.3% on UCF50 and 47.6% on HMDB51.

Phase congruence measurement for image similarity assessment

In the performance assessment of an image processing algorithm, an image is often compared with an available reference. Measuring image similarity can be achieved in many ways; comparison algorithm varies from pixel-based mean square error method to structure-based image quality index. In this paper, we present a new feature-based approach that utilizes image phase congruency measurement to quantify the assessment of the similarities or differences between two images. Test results with standard images and industrial inspection images are presented.

Temporal Synchronization of Video Sequences in Theory and in Practice

In this work, we present a formalization of the video synchronization problem that exposes new variants of the problem that have been left unexplored to date. We also present a novel method to temporally synchronize multiple stationary video cameras with overlapping views that: 1) does not rely on certain scene properties, 2) suffices for all variants of the synchronization problem exposed by the theoretical disseration, and 3) does not rely on the trajectory correspondence problem to be solved apriori. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying inflection points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find moving features that best agree with the pre-computed camera geometries from stationary image features. By using the fundamental matrix and the trifocal tensor in the second refinement step we are able to improve the estimation of the first step and handle a broader range of input scenarios and camera conditions.

Detecting and matching feature points

Abstract This paper proposes a new feature point detector which uses a wedge model to characterize corners by their orientation and angular width. This detector is compared to two popular feature point detectors: the Harris and SUSAN detectors, on the basis of some defined quality attributes. It is also shown how feature points between widely separated views can be matched by using the information provided by the detector to approximate local affine transformations between them.

A morphological operator for corner detection

Abstract This paper presents a new operator for corner detection. This operator uses a variant of the morphological closing operator, which we have called asymmetrical closing. It consists of the successive application of different morphological operators using different structuring elements. Each of these structuring elements used to probe the image under study is tuned to affect corners of different orientation and brightness. We found that this kind of approach, based on brightness comparisons, leads to results of good quality, achieved at a low computational cost.

Feature Based Cut Detection with Automatic Threshold Selection

There has been much work concentrated on creating accurate shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. In this work we present a scheme based on stable feature tracking for inter frame differencing. Furthermore, we present a method to stabilize the differences and automatically detect a global threshold to achieve a high detection rate. We compare our scheme against other cut detection techniques on a variety of data sources that have been specifically selected because of the difficulties they present due to quick motion, highly edited sequences and computer-generated effects.

A feature-based metric for the quantitative evaluation of pixel-level image fusion

Pixel-level image fusion has been investigated in various applications and a number of algorithms have been developed and proposed. However, few authors have addressed the problem of how to assess the performance of those algorithms and evaluate the resulting fused images objectively and quantitatively. In this study, two new fusion quality indexes are proposed and implemented through using the phase congruency measurement of the input images. Therefore, the feature-based measurements can provide a blind evaluation of the image fusion result, i.e. no reference image is needed. These metrics take the advantage of the phase congruency measurement which provides a dimensionless contrast- and brightness-invariant representation of image features. The fusion quality indexes are compared with recently developed blind evaluation metrics. The validity of the new metrics are identified by the test on the fusion results achieved by a number of multiresolution pixel-level fusion algorithms.

Video summarization from spatio-temporal features

In this paper we present a video summarization method based on the study of spatio-temporal activity within the video. The visual activity is estimated by measuring the number of interest points, jointly obtained in the spatial and temporal domains. The proposed approach is composed of five steps. First, image features are collected using the spatio-temporal Hessian matrix. Then, these features are processed to retrieve the candidate video segments for the summary (denoted clips). Further on, two specific steps are designed to first detect the redundant clips, and second to eliminate the clapperboard images. The final step consists in the construction of the final summary which is performed by retaining the clips showing the highest level of activity. The proposed approach was tested on the BBC Rushes Summarization task within the TRECVID 2008 campaign.