In this survey, we give an overview of invariant interest point detectors, how they evolvd over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

https://www.nowpublishers.com/article/DownloadSummary/CGV-017

Local Invariant Feature Detectors: A Survey

The TNO Human Factors Searchu2 image dataset consists of: a set of 44 high-resolution digital color images of different complex natural scenes, the ground truth corresponding to each of these scenes, and the results of psychophysical experiments on each of these images. The images in the Searchu2 dataset are a subset of a larger set that has been used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. The image dataset, an Excel file with the ground truth and observer data, and an extensive report describing the dataset are available on CD-ROM (requests by email to the first author). The dataset can be used to develop and validate digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and models of human visual search and detection. The dataset has already been used in more than ten different studies in the literature, ranging from studies evaluating target detectability metrics to eye movement studies and attempts to model the human visual system. In addition to this work, eight other articles in this special section address the Searchu2 dataset.

/pdf/image-dataset-for-testing-search-and-detection-models-1qljm4idjd.pdf

Image dataset for testing search and detection models

https://www-sop.inria.fr/members/Neil.Bruce/neilbruceCNS04.pdf

Features that draw visual attention: an information theoretic perspective

https://archive-ouverte.unige.ch/unige:47491/ATTACHMENT01

Image analysis and computer vision in medicine.

Attention and memory are very closely related and their aim is to simplify the acquired data into an intelligent structured data set. Two main points are discussed in this paper. The first one is the presentation of a novel visual attention model for still images which includes both a bottom-up and a top-down approach. The bottom-up model is based on structures rarity within the image during the forgetting process. The top-down information uses mouse-tracking experiments to build models of a global behavior for a given kind of image. The proposed models assessment is achieved on a 91-image database. The second interesting point is that the relative importance of bottom-up and top-down attention depends on the specificity of each image. In unknown images the bottom-up influence remains very important while in specific kinds of images (like web sites) top-down attention brings the major information.

Relative Influence of Bottom-Up and Top-Down Attention

A bottom-up attention system for active vision

A recent trend in model-based object recognition is to build efficient systems for primary hypotheses generation. These systems, also called visual indexing, rely on the assumption that object identification can be performed by recovering local invariants. However, reliable and significant local features are difficult to retrieve when the image background is not uniform.

/pdf/temporal-precedence-in-asynchronous-visual-indexing-270qskk4jm.pdf

Temporal Precedence in Asynchronous Visual Indexing

In this paper we propose a new architecture for a general-purpose computer vision system whose design principles have been inspired by the study of human vision. Two important components are an object recognition module and a focus of attention module, respectively called “what” and “where” subsystems. The “what” subsystem is implemented through a set of agents that cooperate towards the interpretation of the image features. The “where” subsystem acts as a control module by detecting locations in the image which contain features that are likely to belong to interesting objects. A succession of attention windows is then generated for such locations and used to gate the parts of the image that are analyzed by the agents.

Visual Indexing with an Attentive System

This paper presents basic principles as well as results obtained with our opportunistic and asynchronous object recognition system. The fundamental idea is to transform a static image into several dataflows of images primitives (e.g line segments, circular arcs, regions). The ranking of the primitives in each flow, that is the delay of each one with respect to the others, is a function of the quantitative relevance of each primitive for the recognition. All flows are then integrated into a common dataflow, thus merging primitives of all types. This common flow is then spatially filtered by attention masks provided by a bottom-up focus of attention mechanism. Doing so, the complexity of the recognition problem is drastically reduced. A top-down, purposive grouping is initiated by relevant (early) primitives in the flow, allowing to better the initial image segmentation. Finally, a dynamic indexing scheme benefits from the asynchronous "arrival" of primitives in the dataflow, by concentrating on the most relevant (early) objects hypotheses. Less relevant hypotheses are delayed in time rather than eliminated, and might be reconsidered after expiration of this delay.

/pdf/selecting-relevant-information-and-delaying-irrelevant-data-2ek109k5in.pdf

Selecting relevant information and delaying irrelevant data for objects recognition

In this paper an attention module is described, which can be used by an active vision system to generate gaze changes. This module is based on a bottom-up, feature-driven analysis of the image. The results are regions of the input image which contain strange features, i.e., locations of the most `interesting' and `important' information. The method proposed for detecting such regions is based on the decomposition of the input image into a set of independent retinotopic feature maps. Each map represents the value of a certain attribute computed on a set of low-level primitives such as contours and regions. Relevant objects can be detected if the corresponding primitives have a feature value strongly different from the neighboring ones. Local comparisons of feature values are used to compute such measures of `difference' for each feature map and give rise to a corresponding set of conspicuity maps. In order to obtain a single measure of interest for each location and to make the process robust to noise, a relaxation algorithm is run on the set of conspicuity maps. A dozen iterations are sufficient to detect a binary mask identifying the attention regions. Results on real scenes are presented.

Relaxation network for a feature-driven visual attention system

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