Object Counting with Deep Learning

This thesis explores various empirical aspects of deep learning or convolutional network based models for efficient object counting. First, we train moderately large convolutional networks on comparatively smaller datasets containing few hundred samples from scratch with conventional image processing based data augmentation. Then, we extend this approach for unconstrained, outdoor images using more advanced architectural concepts. Additionally, we propose an efficient, randomized data augmentation strategy based on sub-regional pixel distribution for low-resolution images. Next, the effectiveness of depth-to-space shuffling of feature elements for efficient segmentation is investigated for simpler problems like binary segmentation -- often required in the counting framework. This depth-to-space operation violates the basic assumption of encoder-decoder type of segmentation architectures. Consequently, it helps to train the encoder model as a sparsely connected graph. Nonetheless, we have found comparable accuracy to that of the standard encoder-decoder architectures with our depth-to-space models. After that, the subtleties regarding the lack of localization information in the conventional scalar count loss for one-look models are illustrated. At this point, without using additional annotations, a possible solution is proposed based on the regulation of a network-generated heatmap in the form of a weak, subsidiary loss. The models trained with this auxiliary loss alongside the conventional loss perform much better compared to their baseline counterparts, both qualitatively and quantitatively. Lastly, the intricacies of tiled prediction for high-resolution images are studied in detail, and a simple and effective trick of eliminating the normalization factor in an existing computational block is demonstrated. All of the approaches employed here are thoroughly benchmarked across multiple heterogeneous datasets for object counting against previous, state-of-the-art approaches

DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

A Novel Pre-processing Technique for DCTdomain Palm-print Recognition

Abstractó In this paper, a novel pre-processing algorithm is introduced to identify the principal lines from a palm-print image and a discrete cosine transform (DCT) domain feature extraction algorithm is then employed for palm-print recognition, which can efficiently capture the spatial variations in the principal lines of a palm-print image. The entire image is segmented into several small spatial modules. The task of feature extraction is carried out in local zones using two dimensional discrete cosine transform (2D-DCT). The proposed dominant DCT-domain feature selection algorithm offers an advantage of very low feature dimension and it is capable of capturing precisely the detail variations within the palmprint image. It is shown that because of the pre-processing step, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods. Index Termsó Spectral feature extraction, binary palm image, two-dimensional discrete cosine transform, classification, palm- print recognition, entropy, modularization. CONVENTIONAL ID card and password based identification methods, although very popular, are no more reliable as before because of the use of several advance