42 research outputs found
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
Accepted for publication in International Journal of Computer Vision and Image Understanding (CVIU)International audienceWe propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that this leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance
An efficient multiscale scheme using local zernike moments for face recognition
In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.WOS:000437326800174Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2 - Q3ArticleUluslararası işbirliği ile yapılmayan - HAYIRMayıs2018YÖK - 2017-1
A comprehensive review of fruit and vegetable classification techniques
Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable
Experimental investigation of inertial particle transport in a turbulent boundary layer
PhDThe first major part of the work was to commission and test the newly built 3 meter openchannel
experimental rig. Various development stages have been carried to improve the design
specifications to meet experimental requirements. The original 2 meter open-channel working
section was replaced with a new 3 meter channel working section enabling measurements to be
taken further downstream allowing the boundary layer to develop. The original bell mouth inlet
was replaced with a hyperbolic tangent profile 3:1 contraction with a honeycomb, coarse and
fine gauzes fitted upstream. 25% porous perforated plates were installed at the channel exit and
also within the inlet plenum tank to reduce the turbulence level. LDV measurement in the freestream
revealed that the turbulence level is below 1% and the boundary layer profile collapses
well with DNS data of Schlatter (2010). A dip in the outer wake region of the velocity profile
can be observed throughout the measurements and is attributed to the aspect ratio of the channel
which is 1.7 at Fr = 0.33. Nevertheless, boundary layer profile and turbulence intensity profile
collapse well with published DNS data. Good agreement was obtained between measurements
carried out using the available LDV and time-resolved PIV systems.
Time-resolved PIV measurements were performed in a dilute particle-laden flow, tracking
nearly neutrally buoyant polymer microspheres within the measured velocity field of a near
wall turbulent boundary layer. Data were taken 2100mm downstream of the inlet, in a vertical
light-sheet aligned in the streamwise direction on the centerline of the horizontal, open-channel
channel facility. High frame-rate measurements were taken to temporally and spatially track
particle motion and instantaneous visualization clearly reveal a link between particle movement
and near-wall coherent structures. Structures having 2D vorticity signatures of near-wall hairpin
vortices and hairpin packets, directly affect particle motion. Statistical and instantaneous results
agree well with published experimental and numerical work.
Conditional statistics were investigated for the particles using the Quadrant method. Particles
moving outwards from the channel floor are influenced by the Quadrant 2 ejection events
and those that moves inwards towards the wall are influenced by the Quadrant 4 sweeps events.
v
Particle-fluid velocity correlations, rpf were calculated for each particle trajectory and averaged
of all the particle-fluid velocity correlations, R, were determined for the whole dataset for
Re=1000. This value is estimated to be 0.0261 and 0.000643 respectively for the particle-fluid
streamwise and wall-normal velocity correlation
Variational and learning models for image and time series inverse problems
Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms
Cytoskeletal Mechanics and Mobility in the Axons of Sensory Neurons
The axon is a long specialized signaling projection of neurons, whose cytoskeleton is composed of networks of microtubules and actin filaments. The dynamic nature of these networks and the action of their associated motor and cross-linking proteins drives axonal growth. Understanding the mechanisms that control these processes is vitally important to neuroregenerative medicine and in this dissertation, evidence will be presented to support a model of interconnectivity between actin and microtubules in the axons of rat sensory neurons. First, the movement of GFP-actin was evaluated during unimpeded axonal outgrowth and a novel transport mechanism was discovered. Most other cargoes in the axon are actively moved by kinesin and dynein motor proteins along stationary
microtubules, or are moved along actin filaments by myosin motor proteins. Actin, however, appears to be collected into short-lived bundles that are either actively carried as cargoes along other actin filaments, or are moved as passive cargoes on short mobile microtubules. Additionally, in response to an applied stretch, the axon does not behave as a uniform visco-elastic solid but rather exhibits local heterogeneity, both in the instantaneous response to
stretch and in the remodeling which follows. After stretch, heterogeneity was observed in both the realized strain and long term reorganization along the length of the axon suggesting local variation in the distribution and connectivity of the cytoskeleton. This supports a model of stretch response in which sliding filaments dynamically break and reform connections within and between the actin and microtubule networks. Taken together, these two studies provide evidence for the mechanical and functional connectivity between actin and microtubules in the axonal cytoskeleton and suggest a far more important role for actin in the development of the peripheral nervous system. Moreover this provides a biological framework for the exploration of future regenerative therapies
Learned Feedback & Feedforward Perception & Control
The notions of feedback and feedforward information processing gained prominence under cybernetics, an early movement at the dawn of computer science and theoretical neuroscience. Negative feedback processing corrects errors, whereas feedforward processing makes predictions, thereby preemptively reducing errors. A key insight of cybernetics was that such processes can be applied to both perception, or state estimation, and control, or action selection. The remnants of this insight are found in many modern areas, including predictive coding in neuroscience and deep latent variable models in machine learning. This thesis draws on feedback and feedforward ideas developed within predictive coding, adapting them to improve machine learning techniques for perception (Part II) and control (Part III). Upon establishing these conceptual connections, in Part IV, we traverse this bridge, from machine learning back to neuroscience, arriving at new perspectives on the correspondences between these fields.</p
Computer vision based classification of fruits and vegetables for self-checkout at supermarkets
The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging.
The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature.
An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness.
Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications