Knowledge-based machine vision systems for space station automation

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed

Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7

Pattern recognition in a multi-sensor environment

Journal ArticleCurrent pattern recognition systems tend to operate on a single sensor, e.g., a camera. however. the need is now evident for pattern recognition systems which can operate in multi-sensor environments. For example, a robotics workstation may use range finders. cameras, tactile pads, etc. The Multi-sensor Kernel System (MKS) provides an efficient and coherent approach to the specification, recovery, and analysis of patterns in the data sensed by such a diverse set of sensors. We demonstrate how much a system can be used to support both feature-based object models as well as structural models. The problems solved is the localization of a three-dimensional object in 3-space. Moreover, MKS allows rapid reconfiguration of the available sensors and the high-level models

A strategy for the visual recognition of objects in an industrial environment.

This thesis is concerned with the problem of recognizing industrial objects rapidly and flexibly. The system design is based on a general strategy that consists of a generalized local feature detector, an extended learning algorithm and the use of unique structure of the objects. Thus, the system is not designed to be limited to the industrial environment. The generalized local feature detector uses the gradient image of the scene to provide a feature description that is insensitive to a range of imaging conditions such as object position, and overall light intensity. The feature detector is based on a representative point algorithm which is able to reduce the data content of the image without restricting the allowed object geometry. Thus, a major advantage of the local feature detector is its ability to describe and represent complex object structure. The reliance on local features also allows the system to recognize partially visible objects. The task of the learning algorithm is to observe the feature description generated by the feature detector in order to select features that are reliable over the range of imaging conditions of interest. Once a set of reliable features is found for each object, the system finds unique relational structure which is later used to recognize the objects. Unique structure is a set of descriptions of unique subparts of the objects of interest. The present implementation is limited to the use of unique local structure. The recognition routine uses these unique descriptions to recognize objects in new images. An important feature of this strategy is the transference of a large amount of processing required for graph matching from the recognition stage to the learning stage, which allows the recognition routine to execute rapidly. The test results show that the system is able to function with a significant level of insensitivity to operating conditions; The system shows insensitivity to its 3 main assumptions -constant scale, constant lighting, and 2D images- displaying a degree of graceful degradation when the operating conditions degrade. For example, for one set of test objects, the recognition threshold was reached when the absolute light level was reduced by 70%-80%, or the object scale was reduced by 30%-40%, or the object was tilted away from the learned 2D plane by 300-400. This demonstrates a very important feature of the learning strategy: It shows that the generalizations made by the system are not only valid within the domain of the sampled set of images, but extend outside this domain. The test results also show that the recognition routine is able to execute rapidly, requiring 10ms-500ms (on a PDP11/24 minicomputer) in the special case when ideal operating conditions are guaranteed. (Note: This does not include pre-processing time). This thesis describes the strategy, the architecture and the implementation of the vision system in detail, and gives detailed test results. A proposal for extending the system to scale independent 3D object recognition is also given

Volumetric Data Classification: A Study Direct at 3-D Imagery

This thesis describes research work undertaken in the field of image mining (particularly medical image mining). More specifically, the research work is directed at 3-D image classification according to the nature of a particular Volume Of Interest (VOI) that appears across a given image set. In this thesis the term VOI Based Image Classification (VOIBIC) has been coined to describe this process. VOIBIC entails a number of challenges. The first is the identification and isolation of the VOIs. Two segmentation algorithms are thus proposed to extract a given VOI from an image set: (i) Volume Growing and (ii) Bounding Box. The second challenge that VOIBIC poses is, once the VOI have been identified, how best to represent the VOI so that classification can be effectively and efficiently conducted. Three approaches are considered. The first is founded on the idea of using statistical metrics, the Statistical Metrics based representation. This representation offers the advantage in that it is straightforward and, although not especially novel, provides a benchmark. The second proposed representation is founded on the concept of point series (curves) describing the perimeter of a VOI, the Point Series representation. Two variations of this representation are considered: (i) Spoke based and (ii) Disc based. The third proposed representation is founded on a Frequent Subgraph Mining (FSM) technique whereby the VOI is represented using an Oct-tree structure to which FSM can be applied. The identified frequent subtrees can then be used to define a feature vector representation compatible with many classifier model generation methods. The thesis also considers augmenting the VOI data with meta data, namely age and gender, and determining the effect this has on performance. The presented evaluation used two 3-D MRI brain scan data sets: (i) Epilepsy and (ii) Musicians. The VOI in this case were the lateral ventricles, a distinctive VOI in such MRI brain scan data. For evaluation purposes two scenarios are considered, distinguishing between: (i) epilepsy patients and healthy people and (ii) musicians and non-musicians. The results indicates that the Spoke based point series representation technique produced the best results with a recorded classification accuracy of up to 78.52% for the Epilepsy dataset and 84.91% for the Musician dataset

Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Population estimation mining from satellite imagery

The collection of census data is an important task with respect to providing support for decision makers. However, the collection of census data is also resource intensive. This is especially the case in areas which feature poor communication and transport networks. In this thesis a number of methods are proposed for collecting census data by applying prediction techniques to relevant satellite imagery. The test site for the work is a collection of villages lying some 300km to the northwest of Addis Ababa in Ethiopia. The idea is to build a predictor that can label households according to “family” size. To this end training data has been obtained by collecting “on ground” census data and matching this up with satellite imagery. The fundamental idea is to segment satellite images so as to obtain satellite sub-images describing individual households and representing these segmentations using a number of proposed representations: graph-based, histogram based and texture based. By pairing each represented household with the collated census data, namely family size, a predictor can be constructed to predict household sizes according to the nature of each representation. The generated predictor can then be used to provide a quick and easy mechanism for the approximate collection of census data that does not require significant resource