Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

Symmetric positive-definite (SPD) matrices are ubiquitous in Computer Vision, Machine Learning and Medical Image Analysis. Finding the center/average of a population of such matrices is a common theme in many algorithms such as clustering, segmentation, principal geodesic analysis, etc. The center of a population of such matrices can be defined using a variety of distance/divergence measures as the minimizer of the sum of squared distances/divergences from the unknown center to the members of the population. It is well known that the computation of the Karcher mean for the space of SPD matrices which is a negatively-curved Riemannian manifold is computationally expensive. Recently, the LogDet divergence-based center was shown to be a computationally attractive alternative. However, the LogDet-based mean of more than two matrices can not be computed in closed form, which makes it computationally less attractive for large populations. In this paper we present a novel recursive estimator for center based on the Stein distance - which is the square root of the LogDet divergence - that is significantly faster than the batch mode computation of this center. The key theoretical contribution is a closed-form solution for the weighted Stein center of two SPD matrices, which is used in the recursive computation of the Stein center for a population of SPD matrices. Additionally, we show experimental evidence of the convergence of our recursive Stein center estimator to the batch mode Stein center. We present applications of our recursive estimator to K-means clustering and image indexing depicting significant time gains over corresponding algorithms that use the batch mode computations. For the latter application, we develop novel hashing functions using the Stein distance and apply it to publicly available data sets, and experimental results have shown favorable comparisons to other competing methods.

Active contour model (ACM) plays an important role in computer vision and medical image analysis. The traditional ACMs were employed to extract closed contours of objects. While simultaneous extraction of line- and block-like objects, such as boundary contours, centerlines, as well as their topological relationship, remains open so far. Therefore, a novel ACM named "Ingenious Snake" is proposed to adaptively extract the feature curves. The proposed ingenious snake includes the following steps: 1) In the preprocessing, the line- and block-like structures are classified with k-means clustering, following up with morphological operation and enhancement. The gradient vector flow (GVF) field is then acquired from the resultant ridge feature map. 2) For the automatic initialization, the ridge-points are extracted by using the local phase measurement of GVF field, then the two-category of object ridgelines are obtained fast. 3) Finally, the contour deformation and curves evolvement are implemented with a management strategy. The resultant contours and centerlines well characterize the objects of interest. In the experiments, we compare the existing initialization methods and the adaptive extraction with a series of phantoms and testing images. The visual and quantitative assessments of structure extraction are satisfying in terms of effectiveness and accuracy.

Classification and recognition of objects for images is an important issue in many scientific fields such as computer vision, biometrics and medical image analysis. An important feature of many objects is shape, so shape analysis has become an important part of classification. One method of shape analysis is to estimate boundaries and analyze the shape of these boundaries to determining the characteristics of the original object. However, many literature studies on point cloud shape analysis are based on existing shapes. This paper mainly refers to Ripley’s K-function in spatial point analysis, through this judgment on spatial distribution of point cloud data to determine the existence of shape in point cloud data, through the spatial distribution of 2D point cloud data and 3D point cloud data. Judging by the randomness of the experiment, K-function has a considerable effect on judging existence of point cloud data shape through relevant experimental verification analysis.

Statistical models of the shape and appearance of deformable objects have become widely used in Computer Vision and Medical Image Analysis. Here we give an overview of such models and of two efficient algorithms for matching such models to new images (Active Shape Models and Active Appearance Models). We also describe recent work on automatically constructing such models from minimally labelled training images.KeywordsMinimum Description LengthFace ModelActive Appearance ModelActive Shape ModelMedical Image AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data.This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed.

Monitoring cameras are now widely used to monitor everything from a room in a house to an entire warehouse. However, in real monitoring scenarios, a variety of factors, such as underexposure, optical blurring, defocusing, have an impact on the quality of images, which leads to low-quality and low-resolution (LR) of the individual of interest. Reconstruction of a high-resolution (HR) face image with detailed facial features, from a LR observation based on a set of HR and LR training image pairs, plays an important role in computer vision and face image analysis applications. To super-resolve an HR face given a LR face image, the key issue is how to effectively encode the LR image patch. However, due to stability and accuracy issues, the coding approaches proposed so far are far from satisfactory. In this paper, we present a novel sparse coding method via exploiting the support information on the coding coefficients. According to the distances between the input patch and bases in the dictionary, we first assign different weights to the coding coefficients and then obtain the coding coefficients by solving a weighted sparse problem. Experiments on commonly used databases and some face images on the real monitoring conditions demonstrate that our method outperforms state-of-the-art.

Assessing sensing techniques for detecting markers of approaching ecdysis in juvenile tropical rock lobsters, Panulirus ornatus

ObjectiveAutomatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.MethodsThis research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.ResultsDue to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.ConclusionA deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Shape modeling is an important area in computer vision and medical imaging. Shape models have proved valuable in the tasks of object representation, recognition, and classification. This thesis focuses on shape models of 3D surfaces extracted from volumetric images and their applications in medical image analysis. This thesis presents a new framework for classification of 3D neuroanatomic structures using shape features, with the goal of deriving a shape-based medical classifier that has clinical value. This framework combines a powerful 3D surface modeling method, the spherical harmonics (SPHARM) description, with a set of effective pattern classification, feature selection, evaluation, and visualization techniques. This approach is shown to be effective using simulated shape data. It is also applied to real hippocampal data, and achieves good cross-validation accuracy (>90%) in detecting hippocampal shape changes in schizophrenia, which is competitive with the best results in previous studies. To help medical diagnosis in practice, a threshold-free receiver operating characteristic approach is employed as a means of evaluating the potential of a classifier. In addition, a new visualization approach is proposed for localizing discriminative patterns. This thesis also considers two extensions of the above work. In one extension, the left and right hippocampi are treated as a single shape configuration, and a new multi-surface alignment algorithm is developed for aligning configurations of multiple surfaces simultaneously. This alignment algorithm is incorporated into our classification for hippocampal data. The best cross-validation accuracy achieved is 92%, supporting the idea that the shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. In the other extension, we focus on spherical parameterization, which is the key step for deriving a SPHARM description for a 3D object. Earlier techniques can be applied only to voxel surfaces. We propose CALD, a new spherical parameterization algorithm which makes our framework applicable to general triangle meshes. This new algorithm can be used for shape representation and manipulation purposes in different areas, including medical image analysis, computer vision, graphics, and multimedia databases.

In the past decade, people's interest in computer vision has grown. Stable multiplication rate boost with powerful computing power every 13 months, face detection and recognition has changed from esoteric concept to popular One of the better and successful applications of computer vision research and image analysis and image analysis Algorithm-based understanding. In our work, we will be detecting contours (shapes) of various geometrical figures in the sample given binary mask extracted from HSV (Hue Saturation Value) range using Python 3.6, Open-Source Computer Vision Library (OpenCV 3x) and Numpy. The very fundamental functions are used for processing the frames, that involves getting live video feed, loading them frame-by-frame, and detecting various shape of red colors inside the live feed frames and segment them with previously saved image with the exact same location. In this paper we discuss three primary problems of computer vision: Finding contours, detection of specified color and segment with another image. We tried state-of-the-art algorithms for these three tasks. HSV color extraction is used to get the mask of desire colored object. In order to get high quality image segmentations results, we applied Morphological operations to refine the mask such as, Opening which removes unnecessary details which in our case are the white region on the boundary of the cloth that is not required, Erosion, Dilatation operation and Gaussian Blurring to smoothen the result. We link this contour detector with Convex Hull, this strategy performs better than our existing image segmentation algorithms on measures of both boundary and segmentation quality. Finally, we replaced pixel value of red color region detected by HSV mask with the pixel value of our background image with the help of bitwise operation to get the augmented result of invisible cloak.

An efficient motion estimator with application to medical image registration

Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification

Image registration is an important problem for image processing and computer vision with many proposed applications in medical image analysis.^{1, 2} Image registration techniques attempt to map corresponding features between two images. The problem is particularly difficult as anatomy is subject to elastic deformations. This paper considers this problem in the context of graph matching. Firstly, weighted Region Adjacency Graphs (RAGs) are constructed from each image using an approach based on watershed saliency. ³ The vertices of the RAG represent salient regions in the image and the (weighted) edges represent the relationship (bonding) between each region. Correspondences between images are then determined using a weighted graph matching method. Graph matching is considered to be one of the most complex problems in computer vision, due to its combinatorial nature. Our approach uses a multi-spectral technique to graph matching first proposed by Umeyama⁴ to find an approximate solution to the weighted graph matching problem (WGMP) based on the singular value decomposition of the adjacency matrix. Results show the technique is successful in co-registering 2-D MRI images and the method could be useful in co-registering 3-D volumetric data (e.g. CT, MRI, SPECT, PET etc.).

Attribute hierarchy based multi-task learning for fine-grained image classification

Polygonal approximation (PA) is a challenging problem in representation of images in computer vision, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. This technique customizes a kind of randomization by searching a set of solutions. In contrast, PA requires more combination of approximation to find an optimal solution. The algorithm involves several steps to produce better results. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem. While compared to other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multi-swarm nature of FA allows finding multiple optimal solutions concurrently. This technique achieves the main goal of PA that is minimum error value with less number of dominant points. The experimental results show that proposed algorithm generates better solutions compared to other algorithms.KeywordsPolygonal approximationFirefly algorithmError valueDominant pointMulti-swarm natureVelocitiesOptimal solutions