A survey on deep learning techniques for image and video semantic segmentation

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.This work has been funded by the Spanish Government TIN2016-76515-R funding for the COMBAHO project, supported with Feder funds. It has also been supported by a Spanish national grant for PhD studies FPU15/04516 (Alberto Garcia-Garcia). In addition, it was also funded by the grant Ayudas para Estudios de Master e Iniciacion a la Investigacion from the University of Alicante

Probabilistic and Deep Learning Algorithms for the Analysis of Imagery Data

Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework for the analysis of the NAIP dataset which includes (1) an unsupervised segmentation module based on the Statistical Region Merging algorithm, (2) a feature extraction module that extracts a set of standard hand-crafted texture features from the images, (3) a supervised classification algorithm based on Feedforward Backpropagation Neural Networks, and (4) a structured prediction framework using Conditional Random Fields that integrates the results of the segmentation and classification modules into a single composite model to generate the final class labels. Next, we introduce two new datasets SAT-4 and SAT-6 sampled from the NAIP imagery and use them to evaluate a multitude of Deep Learning algorithms including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) for generating class labels. Finally, we propose a learning framework by integrating hand-crafted texture features with a DBN. A DBN uses an unsupervised pre-training phase to perform initialization of the parameters of a Feedforward Backpropagation Neural Network to a global error basin which can then be improved using a round of supervised fine-tuning using Feedforward Backpropagation Neural Networks. These networks can subsequently be used for classification. In the following discussion, we show that the integration of hand-crafted features with DBN shows significant improvement in performance as compared to traditional DBN models which take raw image pixels as input. We also investigate why this integration proves to be particularly useful for aerial datasets using a statistical analysis based on Distribution Separability Criterion. Then we introduce a new dataset called noisy-MNIST (n-MNIST) by adding (1) additive white gaussian noise (AWGN), (2) motion blur and (3) Reduced contrast and AWGN to the MNIST dataset and present a learning algorithm by combining probabilistic quadtrees and Deep Belief Networks. This dynamic integration of the Deep Belief Network with the probabilistic quadtrees provide significant improvement over traditional DBN models on both the MNIST and the n-MNIST datasets. Finally, we extend our experiments on aerial imagery to the class of general texture images and present a theoretical analysis of Deep Neural Networks applied to texture classification. We derive the size of the feature space of textural features and also derive the Vapnik-Chervonenkis dimension of certain classes of Neural Networks. We also derive some useful results on intrinsic dimension and relative contrast of texture datasets and use these to highlight the differences between texture datasets and general object recognition datasets

Robust inversion and detection techniques for improved imaging performance

Thesis (Ph.D.)--Boston UniversityIn this thesis we aim to improve the performance of information extraction from imaging systems through three thrusts. First, we develop improved image formation methods for physics-based, complex-valued sensing problems. We propose a regularized inversion method that incorporates prior information about the underlying field into the inversion framework for ultrasound imaging. We use experimental ultrasound data to compute inversion results with the proposed formulation and compare it with conventional inversion techniques to show the robustness of the proposed technique to loss of data. Second, we propose methods that combine inversion and detection in a unified framework to improve imaging performance. This framework is applicable for cases where the underlying field is label-based such that each pixel of the underlying field can only assume values from a discrete, limited set. We consider this unified framework in the context of combinatorial optimization and propose graph-cut based methods that would result in label-based images, thereby eliminating the need for a separate detection step. Finally, we propose a robust method of object detection from microscopic nanoparticle images. In particular, we focus on a portable, low cost interferometric imaging platform and propose robust detection algorithms using tools from computer vision. We model the electromagnetic image formation process and use this model to create an enhanced detection technique. The effectiveness of the proposed technique is demonstrated using manually labeled ground-truth data. In addition, we extend these tools to develop a detection based autofocusing algorithm tailored for the high numerical aperture interferometric microscope

System Designs for Diabetic Foot Ulcer Image Assessment

For individuals with type 2 diabetes, diabetic foot ulcers represent a significant health issue and the wound care cost is quite high. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and the status of the wound tissue. This method is potentially inaccurate for wound assessment and requires extra clinical workload. In view of the prevalence of smartphones with high resolution digital camera, assessing wound healing by analyzing of real-time images using the significant computational power of today’s mobile devices is an attractive approach for managing foot ulcers. Alternatively, the smartphone may be used just for image capture and wireless transfer to a PC or laptop for image processing. To achieve accurate foot ulcer image assessment, we have developed and tested a novel automatic wound image analysis system which accomplishes the following conditions: 1) design of an easy-to-use image capture system which makes the image capture process comfortable for the patient and provides well-controlled image capture conditions; 2) synthesis of efficient and accurate algorithms for real-time wound boundary determination to measure the wound area size; 3) development of a quantitative method to assess the wound healing status based on a foot ulcer image sequence for a given patient and 4) design of a wound image assessment and management system that can be used both in the patient’s home and clinical environment in a tele-medicine fashion. In our work, the wound image is captured by the camera on the smartphone while the patient’s foot is held in place by an image capture box, which is specially design to aid patients in photographing ulcers occurring on the sole of their feet. The experimental results prove that our image capture system guarantees consistent illumination and a fixed distance between the foot and camera. These properties greatly reduce the complexity of the subsequent wound recognition and assessment. The most significant contribution of our work is the development of five different wound boundary determination approaches based on different computer vision algorithms. The first approach employs the level set algorithm to determine the wound boundary directly based on a manually set initial curve. The second and third approaches are the mean-shift segmentation based methods augmented by foot outline detection and analysis. These two approaches have been shown to be efficient to implement (especially on smartphones), prior-knowledge independent and able to provide reasonably accurate wound segmentation results given a set of well-tuned parameters. However, this method suffers from the lack of self-adaptivity due to the fact that it is not based on machine learning. Consequently, a two-stage Support Vector Machine (SVM) binary classifier based wound recognition approach is developed and implemented. This approach consists of three major steps 1) unsupervised super-pixel segmentation, 2) feature descriptor extraction for each super-pixel and 3) supervised classifier based wound boundary determination. The experimental results show that this approach provides promising performance (sensitivity: 73.3%, specificity: 95.6%) when dealing with foot ulcer images captured with our image capture box. In the third approach, we further relax the image capture constraints and generalize the application of our wound recognition system by applying the conditional random field (CRF) based model to solve the wound boundary determination. The key modules in this approach are the TextonBoost based potential learning at different scales and efficient CRF model inference to find the optimal labeling. Finally, the standard K-means clustering algorithm is applied to the determined wound area for color based wound tissue classification. To train the models used in the last two approaches, as well as to evaluate all three methods, we have collected about 100 wound images at the wound clinic in UMass Medical School by tracking 15 patients for a 2-year period, following an IRB approved protocol. The wound recognition results were compared with the ground truth generated by combining clinical labeling from three experienced clinicians. Specificity and sensitivity based measures indicate that the CRF based approach is the most reliable method despite its implementation complexity and computational demands. In addition, sample images of Moulage wound simulations are also used to increase the evaluation flexibility. The advantages and disadvantages of three approaches are described. Another important contribution of this work has been development of a healing score based mechanism for quantitative wound healing status assessment. The wound size and color composition measurements were converted to a score number ranging from 0-10, which indicates the healing trend based on comparisons of subsequent images to an initial foot ulcer image. By comparing the result of the healing score algorithm to the healing scores determined by experienced clinicians, we assess the clinical validity of our healing score algorithm. The level of agreement of our healing score with the three assessing clinicians was quantified by using the Kripendorff’s Alpha Coefficient (KAC). Finally, a collaborative wound image management system between the PC and smartphone was designed and successfully applied in the wound clinic for patients’ wound tracking purpose. This system is proven to be applicable in clinical environment and capable of providing interactive foot ulcer care in a telemedicine fashion