Search Results for author: Shoaib Ehsan

Found 37 papers, 5 papers with code

An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments

no code implementations22 Sep 2021 Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations.

Visual Place Recognition

Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices

no code implementations2 Mar 2021 Mihnea-Alexandru Tomită, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods?

Visual Place Recognition

Scene Retrieval for Contextual Visual Mapping

no code implementations25 Feb 2021 William H. B. Smith, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

The second contribution is an algorithm `DMC' that combines our scene classification with distance and memorability for visual mapping.

General Classification Image Retrieval +3

Improving Visual Place Recognition Performance by Maximising Complementarity

no code implementations16 Feb 2021 Maria Waheed, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information.

Visual Place Recognition

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

no code implementations1 Oct 2020 Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan

To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the visual place recognition problem effectively under changing conditions and with significantly reduced memory requirements.

Visual Place Recognition

ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments

no code implementations28 Sep 2020 Mihnea-Alexandru Tomită, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan

In this paper, we present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions.

Visual Place Recognition

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

1 code implementation17 May 2020 Mubariz Zaffar, Sourav Garg, Michael Milford, Julian Kooij, David Flynn, Klaus McDonald-Maier, Shoaib Ehsan

Visual Place Recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints.

Autonomous Navigation Image Retrieval +1

CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition

no code implementations18 Sep 2019 Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Klaus McDonald-Maier

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems).

Image Retrieval Visual Place Recognition

DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level

1 code implementation3 May 2019 Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus McDonald-Maier

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters.

Displaced People Recognition Image Classification

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

no code implementations16 Apr 2019 Mubariz Zaffar, Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Kostas Alexis, Klaus McDonald-Maier

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years.

Visual Place Recognition

Assessing Capsule Networks With Biased Data

no code implementations9 Apr 2019 Bruno Ferrarini, Shoaib Ehsan, Adrien Bartoli, Aleš Leonardis, Klaus D. McDonald-Maier

This paper aims to fill this gap and proposes two experimental scenarios to assess the tolerance to imbalanced training data and to determine the generalization performance of a model with unfamiliar affine transformations of the images.

Object Classification

Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

no code implementations21 Mar 2019 Mubariz Zaffar, Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Klaus McDonald-Maier

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information.

Visual Place Recognition

GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits

no code implementations11 Feb 2019 Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus D. McDonald-Maier

Our hypothesis is that the emotional state of a person -- how positive or pleasant an emotion is, and the control level of the situation by the person -- are powerful cues for perceiving potential human rights violations.

Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition

no code implementations8 Nov 2018 Mubariz Zaffar, Shoaib Ehsan, Michael Milford, Klaus Mcdonald Maier

This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition.

Visual Place Recognition

A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes

1 code implementation7 Nov 2018 Ahmad Khaliq, Shoaib Ehsan, Zetao Chen, Michael Milford, Klaus McDonald-Maier

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes.

Visual Place Recognition

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

1 code implementation5 Jul 2018 Somdip Dey, Grigorios Kalliatakis, Sangeet Saha, Amit Kumar Singh, Shoaib Ehsan, Klaus McDonald-Maier

Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines.

Sensors, SLAM and Long-term Autonomy: A Review

no code implementations4 Jul 2018 Mubariz Zaffar, Shoaib Ehsan, Rustam Stolkin, Klaus McDonald Maier

Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades.

Simultaneous Localization and Mapping

Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images

1 code implementation12 May 2018 Grigorios Kalliatakis, Shoaib Ehsan, Ales Leonardis, Klaus McDonald-Maier

With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.

Representation Learning Transfer Learning

Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?

no code implementations9 Nov 2017 Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.

General Classification Material Classification

Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database

no code implementations24 Sep 2017 Bruno Ferrarini, Shoaib Ehsan, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.

A Paradigm Shift: Detecting Human Rights Violations Through Web Images

no code implementations30 Mar 2017 Grigorios Kalliatakis, Shoaib Ehsan, Klaus D. McDonald-Maier

The growing presence of devices carrying digital cameras, such as mobile phones and tablets, combined with ever improving internet networks have enabled ordinary citizens, victims of human rights abuse, and participants in armed conflicts, protests, and disaster situations to capture and share via social media networks images and videos of specific events.

Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?

no code implementations12 Mar 2017 Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier

We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.

Automatic Selection of the Optimal Local Feature Detector

no code implementations19 May 2016 Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Ales Leonardis, Klaus D. McDonald-Maier

The efficiency and the good accuracy in determining the optimal feature detector for any operating condition, make the proposed tool suitable to be utilized in real visual applications.

A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors

no code implementations19 May 2016 Shoaib Ehsan, Adrian F. Clark, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier

Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed.

Assessing The Performance Bounds Of Local Feature Detectors: Taking Inspiration From Electronics Design Practices

no code implementations17 Oct 2015 Shoaib Ehsan, Adrian F. Clark, Bruno Ferrarini, Naveed Ur Rehman, Klaus D. McDonald-Maier

Since local feature detection has been one of the most active research areas in computer vision, a large number of detectors have been proposed.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

no code implementations17 Oct 2015 Shoaib Ehsan, Adrian F. Clark, Naveed Ur Rehman, Klaus D. McDonald-Maier

Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44. 44%) in the memory requirements.

Memory-Efficient Design Strategy for a Parallel Embedded Integral Image Computation Engine

no code implementations17 Oct 2015 Shoaib Ehsan, Adrian F. Clark, Wah M. Cheung, Arjunsingh M. Bais, Bayar I. Menzat, Nadia Kanwal, Klaus D. McDonald-Maier

In embedded vision systems, parallel computation of the integral image presents several design challenges in terms of hardware resources, speed and power consumption.

Rapid Online Analysis of Local Feature Detectors and Their Complementarity

no code implementations17 Oct 2015 Shoaib Ehsan, Adrian F. Clark, Klaus D. McDonald-Maier

Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features.

Two-sample testing

Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database

no code implementations17 Oct 2015 Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Klaus D. McDonald-Maier

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.

Improved repeatability measures for evaluating performance of feature detectors

no code implementations29 Apr 2015 Shoaib Ehsan, Nadia Kanwal, Adrian F. Clark, Klaus D. McDonald-Maier

The most frequently employed measure for performance characterisation of local feature detectors is repeatability, but it has been observed that this does not necessarily mirror actual performance.

Hardware based Scale- and Rotation-Invariant Feature Extraction: A Retrospective Analysis and Future Directions

no code implementations29 Apr 2015 Shoaib Ehsan, Adrian F. Clark, Klaus D. McDonald-Maier

Scale- and rotation-invariant local feature extraction is a low level computer vision task with very high computational complexity.

Exploring Integral Image Word Length Reduction Techniques for SURF Detector

no code implementations29 Apr 2015 Shoaib Ehsan, Klaus D. McDonald-Maier

This paper also introduces a novel method to achieve integral image word length reduction for SURF detector.

On-Board Vision Processing For Small UAVs: Time to Rethink Strategy

no code implementations27 Apr 2015 Shoaib Ehsan, Klaus D. McDonald-Maier

Research in the last decade has highlighted the potential of vision sensing in this regard.

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