Publications by Radhakrishna Achanta

Related publications (36)

Automated image-based generation of finite element models for masonry buildings

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

SPRINGER2023

Height And Weight Estimation From Unconstrained Images

Radhakrishna Achanta, Ntorina Thanou

We address the difficult problem of estimating the attributes of weight and height of individuals from pictures taken in completely unconstrained settings. We present a deep learning scheme that relies on simultaneous prediction of human silhouettes and sk ...

IEEE2020

Finding Objects of Interest in Images using Saliency and Superpixels

Radhakrishna Achanta

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledg ...

EPFL2011

Generating LOD3 building models from structure-from-motion and semantic segmentation

Pascal Fua, Katrin Beyer, Radhakrishna Achanta, Fernando Perez Cruz, Bryan German Pantoja Rosero, Mateusz Kozinski

This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation tec ...

2022

Partial annotations in active learning for semantic segmentation

Katrin Beyer, Radhakrishna Achanta

Semantic segmentation with deep learning plays a crucial role in various fields, including civil engineering, particularly in tasks such as damage assessment and urban planning. This paper addresses the challenge of efficiently training deep learning model ...

2024

Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns

Radhakrishna Achanta, Amir Rezaie

Under seismic actions, stone masonry buildings are prone to damage. To assess the severity of damaged masonry buildings and their failure modes, engineers connect these problems to surface crack features, such as the crack width and the extent of cracking. ...

ELSEVIER2022

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features

Pascal Fua, Graham Knott, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degene ...

Institute of Electrical and Electronics Engineers2012

Damage-augmented digital twins towards the automated inspection of buildings

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero

Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent development ...

2023

Fourier-Domain Optimization for Image Processing

Sabine Süsstrunk, Radhakrishna Achanta, Majed El Helou, Frederike Dümbgen

Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others. With current image sizes in the order of megabytes, it is extremely exp ...

arXiv2018

Saliency Detection for Content-aware Image Resizing

Sabine Süsstrunk, Radhakrishna Achanta

Content aware image re-targeting methods aim to arbitrarily change image aspect ratios while preserving visually prominent features. To determine visual importance of pixels, existing re-targeting schemes mostly rely on grayscale intensity gradient maps. T ...

2009

Deep Feature Factorization for Concept Discovery

Sabine Süsstrunk, Radhakrishna Achanta, Edo Collins

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical c ...

SpringerLink2018

Method for Generating an HTML Document that Contains Encrypted Files and the Code Necessary for Decrypting Them When a Valid Passphrase is Provided

Radhakrishna Achanta, Thomas Lochmatter

A computer implemented method for encrypting one or more files and wrapping them in an HTML document. The HTML document contains the encrypted files, the necessary code to decrypt the files, as well as user interface code to receive a passphrase input from ...

2015

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero, Amir Rezaie

We present a pipeline for the automatic generation of digital twins of free-standing buildings for damage assessment. Our method takes multiple view images of the target building as input and processes them to produce a 3D model in levels of detail (LOD) f ...

2022

Deep Residual Network for Joint Demosaicing and Super- Resolution

Sabine Süsstrunk, Radhakrishna Achanta, Ruofan Zhou

The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied indepen- dently. That is sub-optimal as sequential processing, demosaic- ing and then super-resolution, may lead to amplification of ar- tifac ...

Society for Imaging Science and Technology ( IS&T )2018

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

Comparison of crack segmentation using digital image correlation measurements and deep learning

Katrin Beyer, Radhakrishna Achanta, Michele Godio, Amir Rezaie

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...

2020

SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi, Appu Shaji

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically c ...

Institute of Electrical and Electronics Engineers2012

Adaptive extreme video completion

Sabine Süsstrunk, Radhakrishna Achanta, Majed El Helou, Ruofan Zhou

A computer-implemented video completion method is proposed for reconstructing a sparsely sampled video comprising sparsely sampled video frames comprising sparse sets of picture element intensity values. The method comprises: distributing the sparse sets o ...

2021

Salient Region Detection and Segmentation

Sabine Süsstrunk, Radhakrishna Achanta

Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. In this paper we present a novel method to determine salient regions in images using low-level features of lumina ...

Springer Berlin / Heidelberg2008

TOPO-Loss for continuity-preserving crack detection using deep learning

Pascal Fua, Katrin Beyer, Radhakrishna Achanta, Fernando Perez Cruz, Bryan German Pantoja Rosero, Doruk Oner

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

2022