The Research of Granular Computing Applied in Image Mosaic

Based on the existing image mosaic technology, this paper introduces the granular computing and obtains a simplified new algorithm. The image mosaic executed by this algorithm at first establishes correlation model on the basis of granular computing theory, and obtains edge map of each image needing mosaic. The new calculation method is used to calculate gradient of in different columns of edge map, to obtain the feature point coordinates with the maximum gradient; meanwhile, all feature points of two images are matched with each other, to acquire the best matching point. In addition, the error-correcting mechanism is introduced in the matching process, which is used to delete feature points with matching error. The correlation calculation is carried out for the matching pixels acquired by the above processing, to get the feature transformational matrix of the two images. According to the matrix, two separated image maps map into the same plane. The slow transitional mosaic method is applied in the aspect of image addition plus overlap removal, so that images have no bulgy boundary after mosaics. The whole image mosaic process shows that the given granular computing algorithm is superior to the traditional one both in the number of processed images and the number of processing, and the mosaic image gained has high quality

Optical Image Blending for Underwater Mosaics

Typical problems for creation of consistent underwater mosaic are misalignment and inhomogeneous illumination of the image frames, which causes visible seams and consequently complicates post-processing of the mosaics such as object recognition and shape extraction. Two recently developed image blending methods were explored in the literature: gradient domain stitching and graph-cut method, and they allow for improvement of illumination inconsistency and ghosting effects, respectively. However, due to the specifics of underwater imagery, these two methods cannot be used within a straightforward manner. In this paper, a new improved blending algorithm is proposed based on these two methods. By comparing with the previous methods from a perceptual point of view and as a potential input for pattern recognition algorithms, our results show an improvement in decreasing the mosaic degradation due to feature doubling and rapid illumination change

Image blending using graph cut method for image mosaicing

In this research work, feature based image mosaicing technique and image blending using graph cut method has been proposed. The image mosaicing algorithms can be divided into two broad categories. The direct method and the feature based method. The first is the direct method or the intensity based method and the second one is based on image features. The direct methods need an ambient initialization whereas, Feature based methods does not require initialization during registration. The feature based techniques are followed by the four primary steps: feature extraction, feature matching, transformation model estimation, image resampling and transformation, and image blending. Harris corner detection, SIFT and SURF are such algorithms which are based on the feature detection for the accomplishment of image mosaicing, but the algorithms has their own limitations as well as advantages according to the applications concerned. The proposed method employs the Harris corner detection algorithm for corner detection. The features are detected and the feature descriptors are formed around the corners. The feature descriptors from one image are matched with other image for the best closeness and only those features are kept, rest are discarded. The transformation model is estimated from the features and the image is warped correspondingly. After the image is warped on a common mosaic plane, the last step is to remove the intensity seam. Graph cut method with minimum cut/ maximum flow algorithm is used for the purpose of image blending. A new method for the optimisation of the cut in the graph cut has been proposed in the research paper

Enhancement of Underwater Video Mosaics for Post-Processing

Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

A process model of children's early verb use

The verb-island hypothesis (Tomasello, 1992) states that children’s early grammars consist of sets of lexically-specific predicate structures (or verb-islands). However, Pine, Lieven and Rowland (1998) have found that children’s early language can also be built around lexical items other than verbs, such as pronouns (this contradicts a strict version of the verb-island hypothesis). This paper presents a computational model (called MOSAIC), which constructs a network of nodes and links based on a performance-limited distributional analysis of the input (mother’s speech). The results show that utterances generated from MOSAIC: (1) more closely resemble the child’s data than the child’s mother’s data on which MOSAIC is trained; and (2) can readily simulate both the verb-island and other-island phenomena which exist in the child’s data

Performance characterization of clustering algorithms for colour image segmentation

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images

Mosaics from arbitrary stereo video sequences

lthough mosaics are well established as a compact and non-redundant representation of image sequences, their application still suffers from restrictions of the camera motion or has to deal with parallax errors. We present an approach that allows construction of mosaics from arbitrary motion of a head-mounted camera pair. As there are no parallax errors when creating mosaics from planar objects, our approach first decomposes the scene into planar sub-scenes from stereo vision and creates a mosaic for each plane individually. The power of the presented mosaicing technique is evaluated in an office scenario, including the analysis of the parallax error

Rethinking the Patch Test for Phase Measuring Bathymetric Sonars

While conducting hydrographic survey operations in the Florida Keys, NOAA Ship Thomas Jefferson served as a test platform for the initial operational implementation of an L-3 Klein HydroChart 5000 Swath Bathymetry Sonar System1 , a hull-mounted phase measuring bathymetric sonar (PMBS). During the project it became apparent that the traditional patch test typically utilized for multibeam echosounder (MBES) systems was poorly suited to the HydroChart – and perhaps other PMBS systems as well. These systems have several inherent characteristics that make it difficult to isolate and subsequently solve for biases under the traditional patch test paradigm: presence of a nadir gap, wide swaths (typically greater than 6 times water depth), and relatively poor object-detection capability in the outer swath. After “rethinking” the patch test to account for these characteristics, the authors propose a new patch test paradigm that is better suited to the HydroChart and other PMBS systems