Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Hardware Accelarated Visual Tracking Algorithms. A Systematic Literature Review

Many industrial applications need object recognition and tracking capabilities. The algorithms developed for those purposes are computationally expensive. Yet ,real time performance, high accuracy and small power consumption are essential measures of the system. When all these requirements are combined, hardware acceleration of these algorithms becomes a feasible solution. The purpose of this study is to analyze the current state of these hardware acceleration solutions, which algorithms have been implemented in hardware and what modifications have been done in order to adapt these algorithms to hardware.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Expectations of elderly people on the nurse-client relationship and interaction in home care : "When I see the nurse's smiling face it feels likes sunshine on a rainy and gloomy day"

The purpose of the thesis was to explore expectations of elderly people on the nurse-client relationship and interaction in home care. The aim is to improve the quality of care to better meet the needs of the clients. A qualitative approach was adopted. Semi-structured theme interviews were used for data collection. The interviews were conducted during spring 2006. Six elderly clients of a private home care company in Southern Finland acted as informants. Content analysis was used as the method of data analysis. The findings suggest that clients expect nurses to provide professional care with loving-kindness. Trust and mutual, active interaction were expected from the nurse-client relationship. Clients considered it important that the nurse recognizes each client's individual needs. The nurse was expected to perform duties efficiently, but in a calm and unrushed manner. A mechanic performance of tasks was considered negative. Humanity was viewed as a crucial element in the nurse-client relationship. Clients expressed their need to be seen as human beings. Seeing beyond the illness was considered important. A smiling nurse was described to be able to alleviate pain and anxiety. Clients hoped to have a close relationship with the nurse. The development of a close relationship was considered to be more likely if the nurse is familiar and genuine. Clients wish the nurses to have a more attending presence. Clients suggested that the work areas of the nurses could be limited so that they would have more time to transfer from one place to another. Clients felt that they would benefit from this as well. The nurses were expected to be more considerate. Clients wished for more information regarding changes that affect their care. They wished to be informed about changes in schedules and plans. Clients hoped for continuity from the nurse-client relationship. Considering the expectations of clients promotes client satisfaction. Home care providers have an opportunity to reflect their own care behaviour on the findings. To better meet the needs of the clients, nurses could apply the concept of loving-kindness in their work, and strive for a more attending presence.