A study of the soil characteristics, weather conditions, and effect of management skills on the yield of the agricultural crop requires site-specific details, which involves large amount of labor and resources, compared to the traditional whole field based analysis. This thesis discusses the design and implemention of yield monitor for sweetpotatoes grown in heavy clay soil. A data acquisition system is built and image segmentation algorithms are implemented. The system performed with an R-Square value of 0.80 in estimating the yield. The other main contribution of this thesis is to investigate the effectiveness of statistical methods and neural networks to correlate image-based size and shape to the grade and weight of the sweetpotatoes. An R-Square value of 0.88 and 0.63 are obtained for weight and grade estimations respectively using neural networks. This performance is better compared to statistical methods with an R-Square value of 0.84 weight analysis and 0.61 in grade estimation.

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This work implements and evaluates different methods of image analysis and manipulation for the purposesof object recognition. It lays the groundwork for possible future projects that could use machine learning onthe output for the purposes of analyzing the behaviour of lab mice. Three different methods are presented,implemented on a selection of examples and evaluated.

This master thesis investigates different approaches for integrating prior information into a neural network for segmentation of medical images. In the study, liver and liver tumor segmentation is performed in a cascading fashion. Context channels in the form of previous segmentations are integrated into a segmentation network at multiple positions and network depths using different integration strategies. Comparisons are made with the traditional integration approach where an input image is concatenated with context channels at a network’s input layer. The aim is to analyze if context information is lost in the upper network layers when the traditional approach is used, and if better results can be achieved if prior information is propagated to deeper layers. The intention is to support further improvements in interactive image segmentation where extra input channels are common. The results that are achieved are, however, inconclusive. It is not possible to differentiate the methods from each other based on the quantitative results, and all the methods show the ability to generalize to an unseen object class after training. Compared to the other evaluated methods there are no indications that the traditional concatenation approach is underachieving, and it cannot be declared that meaningful context information is lost in the deeper network layers.

This work addresses the problem of automatically detecting human skin in images without reliance on color information. Unlike previous methods, we present a new approach that performs well in the absence of such information. A key aspect of the work is that color-space augmentation is applied strategically during the training, with the goal of reducing the influence of features that are based entirely on color and increasing more semantic understanding. The resulting system exhibits a dramatic improvement in performance for images in which color details are diminished. We have demonstrated the concept using the U-Net architecture, and experimental results show improvements in evaluations for all Fitzpatrick skin tones in the ECU dataset. We further tested the system with RFW dataset to show that the proposed method is consistent across different ethnicities and reduces bias to any skin tones. Therefore, this work has strong potential to aid in mitigating bias in automated systems that can be applied to many applications including surveillance and biometrics. / Master of Science / Skin segmentation deals with the classification of skin and non-skin pixels and regions in a image containing these information. Although most previous skin-detection methods have used color cues almost exclusively, they are vulnerable to external factors (e.g., poor or unnatural illumination and skin tones). In this work, we present a new approach based on U-Net that performs well in the absence of color information. To be specific, we apply a new color space augmentation into the training stage to improve the performance of skin segmentation system over the illumination and skin tone diverse. The system was trained and tested with both original and color changed ECU dataset. We also test our system with RFW dataset, a larger dataset with four human races with different skin tones. The experimental results show improvements in evaluations for skin tones and complex illuminations.

Image layers are two-dimensional planes, each comprised of objects extracted from a two-dimensional (2D) image of a scene. Multiple image layers together make up a given 2D image, similar to the way a stack of transparent sheets with drawings together make up a scene in an animation. Extracting layers from 2D images continues to be a difficult task. Image compositing is the process of superimposing two or more image layers to create a new image which often appears real, although it was made from one or more images. This technique is commonly used to create special visual effects in movies, videos and television broadcast. In the widely used "blue screen" method of compositing, a video of a person in front of a blue screen is first taken. Then the image of the person is extracted from the video by subtracting the blue portion in the video, and this image is then superimposed on to another image of a different scene, like a weather map. In the resulting image, the person appears to be in front of a weather map, although the image was digitally created. This technique, although popular, imposes constraints on the object color and reflectance properties and severely restricts the scene setup. Therefore layer extraction and image compositing remains a challenge in the field of computer vision and graphics. In this research, a novel method of layer extraction and image compositing is conceived using a moving-aperture lens, and a prototype of the system is developed. In an image sequence captured with this lens attached to a standard camera, stationary objects in a scene appear to move. The apparent motion in images is created due to planar parallax between objects in a scene. The parallax information is exploited in this research to extract objects from an image of a scene, as layers, to perform image compositing. The developed technique relaxes constraints on object color, properties and requires no special components in a scene to perform compositing. Results from various indoor and outdoor stationary scenes, convincingly demonstrate the efficacy of the developed technique. The knowledge of some basic information about the camera parameters also enables passive range estimation. Other potential uses of this method include surveillance, autonomous vehicle navigation, video content manipulation and video compression. / Ph. D.