Harmonic Networks for Image Classification

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produce features by learning optimal combinations of responses to preset spectral filters. We rely on the use of the Discrete Cosine Transform filters which have excellent energy compaction properties and are widely used for image compression. The proposed harmonic blocks are intended to replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. We demonstrate how the harmonic networks can be efficiently compressed by exploiting redundancy in spectral domain and truncating high-frequency information. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN models results in improved classification performance on CIFAR and ImageNet datasets

Harmonic Convolutional Networks based on Discrete Cosine Transform

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. Using DCT energy compaction properties, we demonstrate how the harmonic networks can be efficiently compressed by truncating high-frequency information in harmonic blocks thanks to the redundancies in the spectral domain. We report extensive experimental validation demonstrating benefits of the introduction of harmonic blocks into state-of-the-art CNN models in image classification, object detection and semantic segmentation applications.Comment: arXiv admin note: substantial text overlap with arXiv:1812.0320

Improving GMM registration with class encoding

Point set registration is critical in many applications such as computer vision, pattern recognition, or in fields like robotics and medical imaging. This paper focuses on reformulating point set registration using Gaussian Mixture Models while considering attributes associated with each point. Our approach introduces class score vectors as additional features to the spatial data information. By incorporating these attributes, we enhance the optimization process by penalizing incorrect matching terms. Experimental results show that our approach with class scores outperforms the original algorithm by [Jian and Vemuri, 2011] in both accuracy and speed

Harmonic Networks for Image Classification

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produce features by learning optimal combinations of responses to preset spectral filters. We rely on the use of the Discrete Cosine Transform filters which have excellent energy compaction properties and are widely used for image compression. The proposed harmonic blocks are intended to replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. We demonstrate how the harmonic networks can be efficiently compressed by exploiting redundancy in spectral domain and truncating high-frequency information. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN models results in improved classification performance on CIFAR and ImageNet datasets

Combining geolocation and height estimation of objects from street level imagery

We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with deterministic output. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network. Computing the object height using our methodology, in addition to object geolocation, has negligible effect on the overall computational cost. Accuracy is demonstrated experimentally for water drains and road signs on which we achieve average elevation estimation error lower than 20cm

Improving GMM registration with class encoding

Point set registration is critical in many applications such as computer vision, pattern recognition, or in fields like robotics and medical imaging. This paper focuses on reformulating point set registration using Gaussian Mixture Models while considering attributes associated with each point. Our approach introduces class score vectors as additional features to the spatial data information. By incorporating these attributes, we enhance the optimization process by penalizing incorrect matching terms. Experimental results show that our approach with class scores outperforms the original algorithm by [Jian and Vemuri, 2011] in both accuracy and speed