Many different low-level feature detectors exist and it is widely agreed that the evaluation of detectors is important. In this paper we introduce two evaluation criteria for interest points: repeatability rate and information content. Repeatability rate evaluates the geometric stability under different transformations. Information content measures the distinctiveness of features. Different interest point detectors are compared using these two criteria. We determine which detector gives the best results and show that it satisfies the criteria well.

Junctions are signi cant features in images with an intensity variation that exhibits multiple orientations. This makes the detection and characterization of junctions a challenging problem.

Abstract—Scene analysis is a major aspect of perception and continues to challenge machine perception. This paper addresses the scene-analysis problem by integrating a primitive segmentation stage with a model of associative memory. Our model is a multistage system that consists of an initial primitive segmentation stage, a multimodule associative memory, and a short-term memory (STM) layer. Primitive segmentation is performed by locally excitatory globally inhibitory oscillator network (LEGION), which segments the input scene into multiple parts that correspond to groups of synchronous oscillations. Each segment triggers memory recall and multiple recalled patterns then interact with one another in the STM layer. The STM layer projects to the LEGION network, giving rise to memory-based grouping and segmentation. The system achieves scene analysis entirely in phase space, which provides a unifying mechanism for both bottom-up analysis and top-down analysis. The model is evaluated with a systematic set of three-dimensional (3-D) line drawing objects, which are arranged in an arbitrary fashion to compose input scenes that allow object occlusion. Memory-based organization is responsible for a significant improvement in performance. A number of issues are discussed, including input-anchored alignment, top-down organization, and the role of STM in producing context sensitivity of memory recall. Index Terms—Associative memory, grouping, integration, locally excitatory globally inhibitory oscillator network (LEGION), scene analysis, segmentation, short-term memory (STM). I.

The problem of figure–ground segmentation is of great importance in both video editing and visual perception tasks. Classical video segmentation algorithms approach the problem from one of two perspectives. At one extreme, global approaches constrain the camera motion to simplify the image structure. At the other extreme, local approaches estimate motion in small image regions over a small number of frames and tend to produce noisy signals that are difficult to segment. With recent advances in image segmentation showing that sparse information is often sufficient for figure– ground segmentation it seems surprising then that with the extra temporal information of video, an unconstrained automatic figure–ground segmentation algorithm still eludes the research community. In this paper we present an automatic video segmentation algorithm that is intermediate between these two extremes and uses spatiotemporal features to regularize the segmentation. Detecting spatiotemporal T-junctions that indicate occlusion edges, we learn an occlusion edge model that is used within a colour contrast sensitive MRF to segment individual frames of a video sequence. T-junctions are learnt and classified using a support vector machine and a Gaussian mixture model is fitted to the (foreground, background) pixel pairs sampled from the detected T-junctions. Graph cut is then used to segment each frame of the video showing that sparse occlusion edge information can automatically initialize the video segmentation problem. 1

In this paper, we present a compositional boosting algorithm for detecting and recognizing 17 common image structures in low-middle level vision tasks. These structures, called “graphlets”, are the most frequently occurring primitives, junctions and composite junctions in natural images, and are arranged in a 3-layer And-Or graph representation. In this hierarchic model, larger graphlets are decomposed (in And-nodes) into smaller graphlets in multiple alternative ways (at Or-nodes), and parts are shared and re-used between graphlets. Then we present a compositional boosting algorithm for computing the 17 graphlets categories collectively in the Bayesian framework. The algorithm runs recursively for each node A in the And-Or graph and iterates between two steps – bottom-up proposal and top-down validation. The bottom-up step includes two types of boosting methods. (i) Detecting instances of A (often in low resolutions) using Adaboosting method through a sequence of tests (weak classifiers) image feature. (ii) Proposing instances of A (often in high resolution) by binding existing children nodes of A through a sequence of compatibility tests on their attributes (e.g angles, relative size etc). The Adaboosting and binding methods generate a number of candidates for node A which are verified by a top-down process in a way similar to Data-Driven Markov Chain Monte Carlo [18]. Both the Adaboosting and binding methods are trained off-line for each graphlet category, and the compositional nature of the model means the algorithm is recursive and can be learned from a small training set. We apply this algorithm to a wide range of indoor and outdoor images with satisfactory results. 1.

Different kinds of local image structures (such as homogeneous, edge-like and junctionlike patches) can be distinguished by the intrinsic dimensionality of the local signals. Intrinsic dimensionality makes use of variance from a point and a line in spectral representation of the signal in order to classify it as homogeneous, edge-like or junction-like. The concept of intrinsic dimensionality has been mostly exercised using discrete formulations; however, a recent work [23, 11] has introduced a continuous definition. The current study analyzes the distribution of local patches in natural images according to this continuous understanding of intrinsic dimensionality. This distribution reveals specific patterns than can be also associated to local image structures established in computer vision and which can be related to orientation and optic flow features. In particular, we link quantitative and qualitative properties of opticflow error estimates to these patterns. In this way, we also introduce a new tool for better analysis of optic flow algorithms.

. In this paper, we propose two Bayesian methods for detecting and

This paper presents a novel method for detecting scale invariant keypoints. It fills a gap in the set of available methods, as it proposes a scale-selection mechanism for junction-type features. The method is a scale-space extension of the detector proposed by Förstner (1994) and uses the general spiral feature model of Bigün (1990) to unify different types of features within the same framework. By locally optimising the consistency of image regions with respect to the spiral model, we are able to detect and classify image structures with complementary properties over scalespace, especially star and circular shapes as interpretable and identifiable subclasses. Our motivation comes from calibrating images of structured scenes with poor texture, where blob detectors alone cannot find sufficiently many keypoints, while existing corner detectors fail due to the lack of scale invariance. The procedure can be controlled by semantically clear parameters. One obtains a set of keypoints with position, scale, type and consistency measure. We characterise the detector and show results on common benchmarks. It competes in repeatability with the Lowe detector, but finds more stable keypoints in poorly textured areas, and shows comparable or higher accuracy than other recent detectors. This makes it useful for both object recognition and camera calibration. 1.

A new object recognition method, the Invariant Pixel Set Signature (IPSS), is introduced. Objects are represented with a probability density on the space of invariants computed from measurements (pixel values) inside convex hulls of n-tuples of interest points. Experimentally the method is tested on COIL-- 20, a publicly available database of 72 views of 20 natural object rotating on a turntable. With a model built from a single view, recognition performance measured by the average match percentile is above ### for ### degrees and above ### for ### degrees. For some object, 100% first rank is achieved for all 72 views. Robustness to occlusion is shown using images with one half covered. For a small change of viewpoint (### degrees) recognition of the occluded object is perfect.

Occlusions are commonplace in man-made and natural environments; they often result in photometric features where a line terminates at an occluding boundary, resembling a "T". We show that the 2-D motion of such Tjunctions in multiple views carries non-trivial information on the 3-D structure of the scene and its motion relative to the camera. We show how the constraint among multiple views of T-junctions can be used to reliably detect them and differentiate them from ordinary point features. Finally, we propose an integrated algorithm to recursively and causally estimate structure and motion in the presence of T-junctions along with other point-features.