Search Results for author: Nadia Kanwal

Found 6 papers, 0 papers with code

A Survey of Modern Deep Learning based Object Detection Models

no code implementations24 Apr 2021 Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, Brian Lee

Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks.

Classification General Classification +2

A Fuzzy Brute Force Matching Method for Binary Image Features

no code implementations20 Apr 2017 Erkan Bostanci, Nadia Kanwal, Betul Bostanci, Mehmet Serdar Guzel

This is mainly due to the image content which is affected by the scene, lighting and imaging conditions.

Comparative Study of Instance Based Learning and Back Propagation for Classification Problems

no code implementations19 Apr 2016 Nadia Kanwal, Erkan Bostanci

The paper presents a comparative study of the performance of Back Propagation and Instance Based Learning Algorithm for classification tasks.

General Classification Imputation

Sensor Fusion of Camera, GPS and IMU using Fuzzy Adaptive Multiple Motion Models

no code implementations9 Dec 2015 Erkan Bostanci, Betul Bostanci, Nadia Kanwal, Adrian F. Clark

A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate.

Motion Estimation Pose Estimation

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.

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.

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